SHORT-TERM ECONOMETRIC FORECASTING ANALYSIS
FOR LATIN AMERICA
THOMAS M. FULLERTON, JR.
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
UNIVERSITY OF FLORIDA LIBRARIES
Professor Carol Taylor West is probably the best known
and most widely published regional economic forecaster in the
world. I was acquainted with her research and professional
reputation long before applying for the senior economist
position on her staff at the Bureau of Economic and Business
Research. Over the past five years, we have faced computer
crashes, parameter instability, hurricane shocks, business
cycle inflection points, submission deadlines, bureaucratic
regulations, jet lag, and mind boggling statistical revisions.
Forecasting Program work was always accomplished in a
responsible manner befitting the tradition of econometric
excellence with which the University of Florida is so well
known. It has been a tremendous experience to forecast the
Florida economy and conduct research with Carol.
David Denslow has been a friend, colleague, and coauthor.
We have spent many hours discussing different policy issues
and business outlooks that one or the other was called upon to
discuss in a public forum. Similarly, Terry McCoy and I have
participated in a number of conferences on topics relating to
policy problems facing Latin America. Terry definitely has
keen insights with respect to the entire region.
Chunrong Ai helped clarify many of the mysteries of
modern econometric theory. He also saved me a lot of time and
effort by indicating which estimation strategies were viable
with respect to Chapter 4 and subsequent research developed
with that data set. Bill Bomberger spent generous amounts of
time going over Chapter 3 and making helpful suggestions.
Prior to his retirement, Henri Theil was on my dissertation
committee. Subsequent to his retirement, Professor Theil has
dropped by my office at least once a week to monitor my
progress and talk about the post-war history of econometrics.
Many other individuals in the College of Business
Administration helped me navigate exams, computer systems, and
Graduate School requirements. In no particular order, they
include Stan Smith, Larry Kenny, Janet Galvez, Tony Tracy, Min
Zhu, Yikang Li, Jennifer Cobb, Jon Hamilton, Richard Romano,
Janet Fletcher, Pam Middleton, Tony Daniele, Janet Rose, Cindy
Houser, Dot Evans, Dian Studstill, David Lenze, and Richard
Lutz. My parents, relatives, and friends provided welcome
relief on numerous occasions. It takes a lot of friendly
advice to get through a doctoral program.
TABLE OF CONTENTS
1 ECONOMETRIC FORECASTING IN LATIN AMERICA .......
2 INFLATIONARY TRENDS IN COLOMBIA ................
2.1 Introduction ...............................
2.2 Previous Research ..........................
2.3 Methodology ................................
2.4 Estimation Results .........................
2.5 Policy Simulation Results..................
2.6 Conclusion .................................
3 SHORT-TERM PRICE MOVEMENTS IN ECUADOR ..........
3.1 Introduction ...............................
3.2 Literature Review..........................
3.3 Theoretical Model ..........................
3.4 Estimation Results .........................
3.5 Policy Simulation Results..................
3.6 Conclusion .................................
4 PREDICTABILITY OF SECONDARY MARKET DEBT PRICES.
4.1 Introduction ...............................
4.2 Earlier Studies ............................
4.3 Empirical Analysis .........................
4.4 Conclusion .................................
5 SUGGESTIONS FOR FUTURE RESEARCH ................
Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
SHORT-TERM ECONOMETRIC FORECASTING ANALYSIS
FOR LATIN AMERICA
Thomas M. Fullerton, Jr.
Chair: Professor Carol Taylor West
Major Department: Economics
Given the short-term nature, one year or less, of many
policy goals espoused by decision makers in Latin America, the
objective of this study is to examine the applicability of
different forecasting techniques to a subset of short-run
economic questions. Because quarterly national income and
product series are typically not available, most commercial
econometric forecasting analysis for the region is conducted
utilizing annual data. Monthly time series do, however, exist
for a large number of key macroeconomic and financial
variables. It is from the latter group of publicly available
data sets that the econometric modeling and simulation
exercises are drawn.
Chapter 2 examines potential impacts associated with
Colombian monetary authority efforts to cut the rate of
inflation by 10 over a 12-month period. Principal tools
relied upon include slower rates of nominal exchange rate
devaluation and money supply growth. Empirical analysis in
Chapter 2 is carried out utilizing transfer function
autoregressive moving average modeling. Model simulations
indicate that adherence to such a program can lead to
noticeable disinflation over a 24-month period.
Similar to other Latin American economies, Ecuador has
faced persistently high rates of inflation in recent years.
In May 1994, the government signed a stand-by loan agreement
with the International Monetary Fund that established a goal
of reducing the inflation rate to 15 percent over a 19-month
period. Chapter 3 develops an exchange rate augmented
monetary model to assess viability of the price stabilization
program. In contrast to the time series approach of Chapter
2, short-run inflationary dynamics are modeled using an
Economic debates regarding Latin America in recent years
have been dominated by the debt crisis. In response to debtor
country defaults, many lenders reduced or reshuffled risk
exposures by selling debt certificates at discounts from face
value. Chapter 4 analyzes the predictability of secondary-
market debt prices for Colombia, Ecuador, and Venezuela.
Estimation is accomplished via generalized least squares over
24 separate historical periods utilizing monthly data. Model
simulations indicate that forecasting these prices is a
ECONOMETRIC FORECASTING IN LATIN AMERICA
Econometric forecasting analysis began developing as a
field of research with the initial endeavors of Jan Tinbergen
and others in the 1930s (Dhane and Barten, 1989). These
relatively small macroeconometric models of the Dutch economy
were developed using annual data. Research on business cycles
in the United States began replicating and extending the Dutch
modeling examples in the 1940s. Increasing interest in short-
term economic fluctuations eventually led to the development
of quarterly forecasting models (Barger and Klein, 1954).
Along with the expansion of structural econometric
modeling and research with respect to forecasting and policy
analysis, time series statistics also became increasingly
sophisticated in the realm of predictive modeling. Most of
these efforts occurred with respect to high frequency monthly
data, especially univariate autoregressive moving average
models (Box and Jenkins, 1976). While often regarded as
competitors, time series models are frequently utilized as
complements to econometric equation sets and can also be
imbedded within a variety of forecasting systems (Clemen,
1989; Fullerton, 1989a; Zellner, 1994; West, 1996).
Latin American macroeconomic forecasting models began to
appear in the 1960s (Beltrdn del Rio, 1991). Similar to the
first models for the Netherlands and the United States, the
early Latin American models utilized systems of simultaneous
equations designed around national income and product account
(NIPA) data. Unlike most industrialized economies, however,
NIPA data in Latin America during this period tended to be
published only at an annual frequency. This constraint
precluded the development of Latin American quarterly
forecasting models in a manner analogous to what occurred in
many industrialized economies.
Although quarterly forecasting models have not been
widely disseminated in Latin America, large-scale forecasting
models built using annual NIPA data abound. Representative
examples include the CIEMEX-WEFA model for Mexico (Beltran del
Rio, 1991), the CIEPLAN model for Chile (Vial, 1988), the
Metroecon6mica model for Venezuela (Palma and Fontiveros,
1988), and the WEFA models for Colombia and Ecuador
(Fullerton, 1993a, 1993b). Among the most distinguishing
characteristics shared by these models are continuous
maintenance and enhancement over sustained periods of time.
All of the aforementioned studies provide detailed references
to the history of macroeconometric modeling in the region.
Econometric forecasting analysis using annual data in Latin
America has a fairly distinguished history and track record
that is well-documented.
Given the volatile behaviors of the majority of the
economies in Latin America in the 1980s, the absence of
quarterly forecasting tools hampered business planning
efforts. Forecasting conferences sponsored by commercial
entities such as Wharton Econometrics during the late 1980s
utilized simulation output from annual structural models for
Latin American countries of interest. Client questions at
these meetings were generally directed toward the first year
of the forecast period, largely ignoring outer period
extrapolation results (for example, see Fullerton, 1991).
This is not to imply that the traditional
macroeconometric Latin American models are regarded as
useless. Most analyses of international indebtedness
typically rely on annual data in order to examine the
consequences of changes in regional balance of payment
factors. Not surprisingly, significant effort was put forth
in recent years to enhance the current account-capital account
linkages and simulation performances in Latin American
macroeconomic models (Fullerton, 1993a). The relative lack of
forecasting models estimated with higher frequency data,
nevertheless, continues to pose an obstacle to corporate,
public sector, and multilateral agency planners and
Although quarterly national income and product account
data are still not widely available to researchers in Latin
America, monthly time series for many economic variables do
exist. Examples of the latter include inflation indices,
money supply measures, currency exchange rates, commodity
export prices, and international reserves. A logical step,
therefore, is to investigate the econometric properties and
predictability of the series presently available at the higher
frequency. The centerpiece chapters of this dissertation
examine three empirical forecasting questions using monthly
data sets from Colombia, Ecuador, and Venezuela. Distinct
estimation techniques are employed in each chapter and
simulation accuracy is summarized for all of the models
Chapter 2 utilizes a transfer function autoregressive
integrated moving average (transfer ARIMA) modeling framework
to analyze movements in consumer prices in Colombia.
Forecasting experiments are conducted with the resulting model
to shed light on potential impacts associated with an anti-
inflationary program enacted by the central bank. Results
indicate that this class of time series modeling can provide
useful insights with respect to macroeconomic trends in Latin
American countries. A revised version of this chapter was
published in the Journal of Policy Modeling (Fullerton,
Chapter 3 also examines the question of forecasting
short-term price movements. Consumer prices in Ecuador are
modeled using an econometric framework that incorporates
domestic macroeconomic factors and international input costs.
Parameter estimation is accomplished with a nonlinear
procedure that provides sufficient flexibility to handle even
mixed error structures. Simulation exercises are also
utilized to examine possible consequences associated with a
price stabilization program implemented by the central bank.
An abridged version of this study was published in Proceedings
of the American Statistical Association, Business and Economic
Statistics Section (Fullerton, 1995a).
Secondary market trades involving sovereign debt
certificates became widespread following the outbreak of the
international debt crisis in 1982. Chapter 4 employs a
generalized least squares modeling strategy to study the
predictability of short-run movements in secondary market debt
prices in Colombia, Ecuador, and Venezuela. As discussed
below, forecast period lengths and information inputs are
selected to reflect considerations facing financial market
participants. A revised version of this chapter appeared in
Applied Economics (Fullerton, 1993d). Additional research
extending the initial results on this topic was presented at
the 65th annual Southern Economic Association conference
INFLATIONARY TRENDS IN COLOMBIA
Inflation has long been one of the most serious economic
problems facing policymakers in Latin America. Although
Colombia has traditionally enjoyed lower rates of inflation
than neighboring South American countries, in 1990, a
presidential election year, consumer prices rose in excess of
32 percent. In response, Colombian monetary authorities
introduced a series of policy innovations designed to lower
the inflation rate. Measures adopted include a progressive
opening of the economy to greater volumes of international
trade, fiscal austerity, tighter credit controls, and a slower
rate of currency devaluation (for details, see Fullerton,
This chapter examines some of the potential results
associated with the principal tools of the policy adjustment
effort. Time series characteristics of the consumer price
index (CPI), the money supply (Ml, defined as currency in
circulation plus demand deposits), and the peso/dollar
exchange rate (REX) are investigated. To measure the short-
run relationships among the CPI, Ml, and REX, transfer
function autoregressive-moving average (ARIMA) analysis is
applied (see Box and Jenkins, 1976, ch. 11).
Selection of the nonlinear model estimation methodology
was motivated by two factors. First was the usefulness of
transfer function ARIMA analysis for short-term forecasting
applications. Second was interest expressed in a previous
study which utilized this technique to analyze price dynamics
in the United States (Fullerton, Hirth, and Smith, 1991).
Specifically, economists within the Research Department of the
Central Bank in Bogota wished to see whether the same approach
would prove useful with respect to the Colombian economy.
Subsequent sections of the paper present a brief review
of the literature, methodology, and empirical results. Policy
simulation exercises designed to reflect exchange rate and
monetary policies in Colombia follow. A summary and
conclusion form the final section of the chapter.
2.2 Previous Research
In one of the earliest time series studies developed for
an economy in Latin America, Cabrera and Montes (1978) utilize
univariate ARIMA techniques to model the CPI in Colombia.
Logarithmic transformation, regular and seasonal differencing
of the monthly CPI series are used to induce stationarity. An
equation containing an autoregressive term at lag 1 and a
seasonal moving average term at lag 12 yields statistically
significant parameter estimates. While simple in structure,
the model exhibits good statistical traits and is found to
simulate historical movements of the CPI successfully.
Empirical evidence is provided that Colombian inflation,
although high relative to many industrial economies, is stable
enough to be modeled and predicted with a fair degree of
Montes and Candelo (1982) propose a simultaneous system
of equations for money, prices, international reserves, and
the exchange rate. Full information maximum likelihood
estimation is used to calculate model parameters which reflect
the hypothesized coefficient restrictions. Although quarterly
data are used, the lag structure of the model is fairly
simple. Domestic monetary conditions and the rate of
devaluation are both found to positively affect consumer
prices in a statistically significant manner. The magnitudes
of the exchange rate coefficients exceed those of the monetary
variables in each of the different sample periods.
Leiderman (1984) utilizes vector autoregressions to
analyze inflation in Colombia and Mexico. Changes in the
rates of growth in the economy and the money supply are
included in each model. In the case of Colombia, the results
indicate that variations in the rate of change of Ml
systematically affect the CPI, but not the converse. From a
policy perspective, this implies that monetary authorities do
not engage in "accommodative" measures in response to
production and inflationary shocks. This result may stem from
the usage of monetary policy to achieve goals other than price
stability. These include economic growth and export
diversification. From an econometric standpoint, this result
is also important because it implies that unidirectional
Granger causality exists between M1 and the CPI in Colombia.
A number of recent studies have examined inflationary
dynamics in the United States. Koch, Rosensweig, and Whitt
(1988) investigate the relationship between the exchange rate
and consumer prices. Cross correlation functions are used to
suggest the number of lags to be included in the regression
equations. Granger causality tests imply a unidirectional
channel of influence from the exchange rate to prices. The
inflationary impacts associated with a change in the
international value of the dollar are found to extend more
than 12 months.
Fullerton, Hirth, and Smith (1991) consider the effects
of exchange rate and other financial and commodity price
variable movements on the CPI in the United States. Transfer
function ARIMA analysis indicates that inflationary impacts
resulting from variations in the exchange rate generally take
more than a year. Credit conditions, as proxied by interest
rate spreads, are found to influence consumer prices
relatively quickly. The slow speed of adjustment from the
exchange rate compared to domestic financial conditions may
reflect incomplete pass-through effects which characterize
large industrial economies.
This is in contrast to what might be expected for a
smaller economy such as Colombia's, where pass-through
effects, statistically significant relationships between
international currency values and inflation, are often strong
and relatively quick (see Leith, 1991). Empirical evidence
reported elsewhere (Edwards, 1985) indicates that movements in
the rate of devaluation are helpful in modeling nominal
interest rates in Colombia. Unfortunately, the latter study
does not directly test the relationship between prices and the
peso/dollar exchange rate.
The methodology utilized in this paper is similar to the
multiple ARIMA approach applied to the United States by
Fullerton, Hirth, and Smith (1991). This technique does not
apply any a priori modeling restrictions on the equations
estimated. It is a useful procedure for investigating high
frequency time series data because it can accommodate
different lag structures in a flexible and efficient manner.
Equations developed using this approach are also easy to
simulate and can help analyze policy proposals.
Univariate ARIMA equations are estimated for the
stationary components of the CPI, Ml, and REX series. The
results are then used to specify and estimate an ARMA transfer
function. A weakly stationary series is defined as one whose
mean and variance do not change over time. Stationarity in
the means of the series is attained through regular and
seasonal differencing. Logarithmic transformations are used
to induce homoscedasticity (see Pankratz, 1983).
The general form of a univariate ARIMA model estimated
for the CPI can be written as follows:
(2.1) Pt = [Q(B)QS(B)Ut] / [H(B)Hs(B)]
where Pt is a stationary series derived from the original CPI
series, B is a backshift or lag operator, Q(B) is a moving
average polynomial of order q, QS(B) is a seasonal moving
average polynomial of order qS, Ut is the disturbance term,
H(B) is an autoregressive polynomial of order p, and Hs(B) is
a seasonal autoregressive polynomial of order pS. The ARMA
model for the CPI is also used in the estimation of the
transfer function equations.
To examine the effects of other variables on the CPI,
transfer function ARIMA models are estimated. These models
are used to determine if the input series are incrementally
useful in explaining the variation of the CPI beyond the
information obtained within the price index itself (see Box
and Jenkins, 1976, ch. 11). Prior to estimating a transfer
function equation, cross correlation functions (CCF) are used
to indicate which lags of an input series may contribute
incremental information to the univariate ARMA model already
estimated for the CPI. Residuals from the three univariate
equations are used to calculate CCFs between the CPI and the
other variables (see Chatfield, 1984, p. 173).
Statistical, or Granger, causality is assumed to be
unidirectional in transfer ARIMA models. If this assumption
is reasonable, movements in REX and M1 will chronologically
precede statistically significant changes in CPI. The
converse will not hold if causality is unidirectional. To
examine the possibility that feedback or endogeneity exists
between the CPI and the other series, ordinary least squares
regression is used to construct Granger causality F-tests.
The general form of the transfer function can be written
in the following manner:
(2.2) Pt = [W(B)Rt + G(B)Mt + Q(B)Qs(B)Ut] / [H(B)Hs(B)]
where the univariate model presented in Equation 2.1 is
augmented by incorporating general order polynomials, W(B) and
G(B), for the respective input variables. Rt and M. represent
stationary input series derived from the exchange rate and the
money supply data discussed above. Because the analysis is
conducted within a dynamic framework, coefficient restrictions
are not hypothesized, but the sums of the coefficients
associated with each individual input series are expected to
2.4 Estimation Results
Data used in this chapter are from the central bank in
Bogota. Monthly observations for all three series are
published in Revista del Banco de la Rep~blica (for example,
see Cabrera and Montes, 1978). The sample period, January
1967 through December 1990, corresponds to the crawling peg
era of exchange rate determination in Colombia.
Similar to Cabrera and Montes (1978), a logarithmic
transformation of the CPI is taken prior to regular and
seasonal differencing to obtain stationarity. The same steps
are taken with respect to REX and Ml prior to modeling.
Results of the stationarity tests for all three series appear
in Table 2.1. The unit root tests are performed with both
constant and trend terms.
Autocorrelation and partial autocorrelation functions
suggested the forms of the univariate ARIMA equations reported
in Table 2.2. Despite the presence of 11 years of additional
data, the results confirm the AR(l), SMA(12) specification
employed by Cabrera and Montes (1978, p. 1137) to model the
CPI. Parameter estimates reported in Model 2.3 carry the same
signs and are similar in magnitude to those estimated in the
previous study (0.450 versus 0.645, and -0.924 versus -0.858).
Equation 2.3 is incorporated in the estimation of the transfer
Table 2.1: Unit Root Tests for Stationarity
Series Aug Dickey-Fuller t-stat MacKinnon crit value
P 9.081 (with const, trend, 1 lag) -4.001 (1% lvl)
R 5.252 (with const, trend, 1 lag)
M -13.195 (with const, trend, 1 lag)
Table 2.2: Univariate ARIMA Models
Q(38) = 52.222
+ 0. 795*Rt1
Q(38) = 31.893
- 0. 634*Ut12
Q(38) = 59.339
The sample period is January 1967 December 1990.
Numbers in parentheses are computed t-statistics.
Ljung-Box Q-statistics calculated for 38 lags are reported.
Ordinary least squares F-tests are used to determine if
unidirectional Granger causality exists between consumer
prices and the two input series. Results for the F-tests,
calculated for 12 and 18 lags, are reported in Table 2.3. The
tests constructed to examine the relationship between the CPI
and M1 utilize seven years of additional data, but support the
conclusions reported by Leiderman (1984). Because monetary
policy in Colombia appears to be conducted independently of
inflationary shocks, the transfer function methodology can be
used to measure the effect of the money supply on inflation.
Similar to results reported for the United States (Koch,
Rosensweig, and Whitt, 1988), the causality tests for the CPI
and REX series indicate that changes in the inflation rate do
not precede systematic variations in the exchange rate. From
an historical perspective, the result is not surprising.
There have been several episodes during the crawling peg era
in Colombia when authorities have permitted the exchange rate
to become overvalued by failing to devalue the local currency
quickly enough to account for inflationary differentials with
major trading partners. Typically, this has tended to take
place following "coffee bonanzas" when Colombian international
reserves are high due to strongly positive merchandise trade
surpluses (Kamas, 1986; Ocampo, 1983).
Table 2.3: Granger Causality Tests
Causality Direction Number of Lags Computed F-stat
CPI => REX 12 0.834
CPI => REX 18 0.799
CPI => M1 12 0.009
CPI => M1 18 0.278
Degrees of freedom for the regressions with 12 lags:
12 for the numerator and 263 for the denominator.
Degrees of freedom for the regressions with 18 lags:
18 for the numerator and 251 for the denominator.
There have also been periods when local price changes
have moved in concert with those of Colombia's trading
partners, rendering unnecessary any modification in the
crawling peg. As a result, the rate of devaluation has not
always been adjusted proportionately to variations in the
domestic rate of inflation. From an economic policy
perspective, the results in Table 2.3 indicate that Colombian
monetary authorities take into account goals and variables
other than domestic inflation when determining rate of
devaluation embodied in the crawling peg for the peso.
Econometrically, this implies that transfer function ARIMA
analysis can be used to model the influence of the
international value of the peso on domestic prices.
Similar to the CPI, stationarity in the REX and M1 series
is attained after logarithmic transformation, and seasonal and
regular differencing. Unit root tests for both variables are
reported in Table 2.1. As noted above, univariate equations
for the exchange rate and money supply series appear in Table
2.2. These equations also exhibit good statistical traits
such as high computed t-statistics and relativley low Q-
statistics. Residuals from the three univariate models were
used to estimate CCFs containing 18 lags. Both CCFs indicated
that the principal effects resulting from a change in either
input variable are incorporated in the CPI within a relatively
Transfer function equations are reported in Table 2.4.
Models 2.6 and 2.7 include only one independent variable, REX
and Ml, respectively. Model 2.8 includes both input series.
The exchange rate is included with lags of 2 and 10 months.
The money supply is included with a 9-month lag.
Autoregressive terms at lag 1 and seasonal moving average
terms at lag 12 are included in all three equations. Similar
to Montes and Candelo (1982), the exchange rate input
coefficients are larger than that of Ml.
Inclusion of the input series improves the Q-statistic
estimated from the residuals associated with each equation.
Virtually all of the improvement in the white noise test
results from the introduction of the lagged stationary
exchange rate data. Coefficients estimated for these series
are statistically significant in Model 2.6 and Model 2.8. The
lagged stationary component of the money supply exhibits the
hypothesized sign, but is not significant at the 5-percent
Because money is a useful predictor of inflation in
Colombian macroeconometric models using annual data
(Fullerton, 1993a), the results encountered in this chapter
are unexpected and further research is warranted. Another
possibility is that M1 may not be the correct money stock
measure with respect to Colombian price dynamics. Studies
completed for other economies indicate that broader liquidity
aggregates such as M2 may be useful (Hallman, Porter, and
Table 2.4: ARIMA Transfer Functions
Q(38) = 39.826
Q(38) = 51.777
Q(38) = 39.389
The sample period is January 1967 December 1990.
Numbers in parentheses are computed t-statistics.
Ljung-Box Q-statistics calculated for 38 lags are reported.
Small, 1991, and Ikhide and Fullerton, 1995). Unfortunately,
money supply estimates other than M1 currently do not exist
for Colombia. This problem is fairly widespread in South
America and has long posed difficulties in the analysis of
monetary economics in the region (see Fullerton and Kapur,
2.5 Policy Simulation Results
The Gaviria administration announced in December 1990
that it would attempt to lower the inflation rate to 22
percent in only twelve months (for discussion, see Fullerton,
1991). To attain this goal, two principal tools were to be
employed. After an extended period of aggressive devaluation,
the nominal rate of devaluation for the peso/dollar exchange
rate was to be reduced. Policymakers also announced that
growth in the money supply would be limited to 22 percent.
To examine the potential effects of these policy
innovations on the CPI, simulations are created using Equation
2.8. In the first exercise, the 12-month growth rate for Ml
is assumed to be immediately limited to 22 percent for an
initial 12-month period, and then lowered to 19 percent the
following year. Similarly, the 12-month crawling peg rate of
devaluation is assumed to be held at 22 percent throughout the
first year, and later be revised downward to 19 percent during
the following year. The 19 percent rates of change are chosen
to reflect longer-term policy goals discussed by the
government, including eventual attainment of a 15 percent
annual rate of change for consumer prices (Fullerton, 1991).
Simulating Model 2.8 under these policy assumptions
yields several interesting results that are reproduced in
Table 2.5. During the first six months, the inflation rate
oscillates near 29 percent. Subsequently, the 12-month rate
of change in the CPI declines sharply to 24 percent by year-
end. During the next 12 months, disinflation subsides as the
severity of the cutbacks in the rates of change of both input
variables is moderated. Year-end inflation declines to 22
percent under this scenario. As will be illustrated below,
not all of the 10-point improvement can be attributed to the
government policy package.
Not surprisingly, Colombian monetary authorities did not
introduce the exchange rate and credit policy changes in the
abrupt manner depicted above. Accordingly, Equation 2.8 is
also simulated under an alternative set of assumptions whereby
intermediate policy steps are implemented more gradually.
Additionally, actual exchange rate and money supply data for
the first six months of 1991 are employed. These data reflect
slower attainment of the new policy goals espoused by the
Finance Ministry and the Central Bank. Subsequent movements
in the 12-month rates of change for the exchange rate and the
money supply are assumed to steadily decline to 19 percent by
December 1992 for both variables.
Table 2.5: Immediate Implementation Policy Simulation Results
Month PCHYA(CPI) PCHYA(REX) PCHYA(MI)
1 32.1 22.0 22.0
2 30.7 22.0 22.0
3 28.9 22.0 22.0
4 29.1 22.0 22.0
5 29.6 22.0 22.0
6 29.2 22.0 22.0
7 28.9 22.0 22.0
8 27.9 22.0 22.0
9 26.8 22.0 22.0
10 26.2 22.0 22.0
11 25.2 22.0 22.0
12 24.1 22.0 22.0
13 23.5 19.0 19.0
14 23.3 19.0 19.0
15 22.5 19.0 19.0
16 22.5 19.0 19.0
17 22.4 19.0 19.0
18 22.4 19.0 19.0
19 22.4 19.0 19.0
20 22.4 19.0 19.0
21 22.4 19.0 19.0
22 22.3 19.0 19.0
23 22.1 19.0 19.0
24 22.1 19.0 19.0
Results of the second simulation exercise are reported in
Table 2.6. The more rapid rate of depreciation allows the 12-
month inflation rates to remain above 30 percent throughout
the first semester of the test period. Steady declines in the
rates of change for both REX and M1 force inflation to decline
noticeably during the next six months, eventually reaching 25
percent. Year-end inflation slows to 22 percent during the
subsequent 12-months of the simulation period, when growth
rates for the input variables gradually decline to 19 percent.
It is useful to note that the second set of medium-range
simulation results reported in Table 2.6 do not vary
significantly from those obtained in the first exercise.
Despite imperfect implementation efforts, these results
indicate that the government can still attain its stated
policy goals. Because a 24-month period is still needed to
lower the rate of change in the CPI by 10 percentage points,
the government policy claims are again shown to be slightly
optimistic. Furthermore, as shown in the final simulation
exercise presented in Table 2.7, much of the improvement in
the inflation rate cannot be attributed to policy design
Of course, policy indecision can also result in no
progress being made toward achieving either intermediate
target. To examine the potential consequences associated with
such an eventuality, model simulations were also tested in
Table 2.6: Gradual Implementation Policy Simulation Results
Month PCHYA(CPI) PCHYA(REX) PCHYA (Ml)
Table 2.7: No Implementation Policy Simulation Results
Month PCHYA(CPI) PCHYA(REX) PCHYA (Ml)
1 32.2 30.0 30.0
2 31.9 30.0 30.0
3 31.6 30.0 30.0
4 31.2 30.0 30.0
5 30.9 30.0 30.0
6 30.6 30.0 30.0
7 30.3 30.0 30.0
8 29.9 30.0 30.0
9 29.6 30.0 30.0
10 29.4 30.0 30.0
11 29.2 30.0 30.0
12 28.8 30.0 30.0
13 28.5 30.0 30.0
14 28.3 30.0 30.0
15 28.0 30.0 30.0
16 27.7 30.0 30.0
17 27.4 30.0 30.0
18 27.1 30.0 30.0
19 26.8 30.0 30.0
20 26.6 30.0 30.0
21 26.4 30.0 30.0
22 26.2 30.0 30.0
23 26.3 30.0 30.0
24 26.2 30.0 30.0
which the growth rates of the input series were held constant
at 30 percent per year. In the absence of any change in the
conduct of monetary management and depreciation rate
determination, the annual rate of inflation stabilizes at 26
percent by the end of the 24-month simulation period. That
rate is well above the announced government target range.
More importantly, the 6-point improvement which results
in a simulation exercise in which no progress is made with
respect to the intermediate policy targets. This result
indicates that only 40 percent of policy attainment embodied
in Tables 2.5 and 2.6 is by government design. The remaining
six-tenths of the 10-point reduction in the annual rate of
change in consumer prices would, on the basis of the above
modeling and simulation framework, have resulted anyway.
In response to growing inflationary pressures, economic
policymakers in Colombia announced that the inflation rate
would be slashed by 10 percentage points to 22 percent over a
12-month period. Two principal tools were selected to foster
disinflation. The nominal rate of devaluation for the
peso/dollar exchange rate was cut and the rate of growth of
the money stock was reduced.
This chapter examines the empirical relationship between
those variables and the consumer price index. Econometric
results are similar to those reported in previous studies for
Colombia and the United States. Transfer ARIMA functions are
estimated and simulated to determine the potential impacts of
the new policies. These exercises indicate that substantial
progress in the anti-inflationary program may be attained
following the implementation of said policy efforts, although
not as quickly as stated by government officials. More
importantly, over half of the 10-point gain results even if
the rates of nominal currency devaluation and money supply
expansion are held constant.
Because of structural economic and administrative policy
changes taking place in Colombia, additional research will
eventually be necessary. Future studies may find it useful to
consider the effects of other variables such as wage rates,
industrial capacity utilization, and commodity prices on the
CPI. Given the insignificant, at the 5-percent level,
coefficient associated with M1, model estimation utilizing
alternative series designed to reflect monetary conditions may
also prove helpful. Introduction of new variables such as
wage rates may necessitate usage of an estimation technique
different from the transfer function methodology described
above. This is due to the possibility that feedback effects,
or simultaneity, may exist between the CPI and other potential
At present, the Colombian economy is being opened to
international trade and a free-market exchange rate system is
slated to be implemented. These policy innovations could
potentially render the above parameter estimates obsolete.
ARIMA intervention analysis (Box and Tiao, 1975) may prove
beneficial in subsequent empirical research designed to
examine this possibility. It is interesting to note, however,
that the similarities between the empirical results reported
in this paper and those analyzed in earlier studies indicate
that agent responses have generally been relatively inelastic
with respect to changing monetary and exchange rate policies
SHORT-TERM PRICE MOVEMENTS IN ECUADOR
Similar to other Latin American economies, Ecuador has
faced persistently high rates of inflation in recent years.
Although inflation was substantially lower in 1993 and 1994,
excessive money supply growth in early 1995 clouded prospects
for additional short-term improvements. Prior to the first-
quarter 1995 border skirmish with Peru, the government had
signed a stand-by loan agreement with the International
Monetary Fund that established a goal of reducing the
inflation rate to 15 percent over a 19-month time frame (Banco
del Pacifico, 1994a). Not withstanding government assurances
that inflation would decelerate to its target rate by year-end
1995, very little econometric analysis using short-term
forecasting methods appears to have been relied upon in
developing the new policy targets.
In its attempt to slow price movements, the Duran Ballen-
Dahik Garzozi administration introduced a variety of new
policy measures. They include import liberalization, fiscal
austerity, and a slower rate of currency depreciation. By
reducing price pressures, the government hopes to improve
economic welfare by enabling the Ecuadorian economy to operate
more efficiently. This argument is very similar to those
aired in advanced economies such as the United States (Motley,
1993) and analyzed in other developing nations (Zind, 1993).
What is unique, however, is the magnitude of the
disinflationary goals set by the Ecuadorian policymakers. As
a result, short-run price stabilization has become the center
piece of government policy efforts in Ecuador.
This chapter examines potential results associated with
the two principal adjustment tools, money supply growth and
exchange rate movements. Despite ongoing difficulties with
inflationary uncertainty and the ambitious nature of current
policy goals, careful econometric analysis of short-run price
movements in Ecuador has not previously been conducted. To
bridge this gap, a modeling framework is proposed, tested, and
used to develop policy simulation exercises for monthly
Ecuadorian price data.
In contrast to the time series methodology utilized for
Colombia, an econometric approach is followed in the analysis
conducted for Ecuadorian price movements. Selection of this
alternative approach was motivated by two factors. First was
a discussion on quantitative analysis of developing country
inflation held at the 32nd International Atlantic Economic
Conference. Second was interest expressed by economists at
the Research Department of the Central Bank in Quito with
respect to attempting to develop a short-run inflationary
model similar to the long-run monetary-import cost approach
incorporated in Fullerton (1993b). Subsequent sections of
this chapter offer a review of the literature, theoretical
model, and empirical results. Suggestions for future research
are summarized in the conclusion.
3.2 Literature Review
The seminal research on inflationary dynamics in
developing countries was conducted on Chilean data by
Harberger (1963). That early paper interestingly points out
that analyzing nominal data in level form could result in
spurious correlations in equations estimated for highly
inflationary economies. To circumvent this problem,
percentage rates of change are utilized in a linear regression
framework based on the quantity theory of money. What became
known as the "Harberger" framework incorporates real income,
current and lagged values of the money supply, and the
opportunity cost of holding cash balances.
The success of this initial effort conducted on Chilean
data spurred a series of replicated studies for other
developing countries. Vogel (1974) estimates an inflation
equation for several Latin American economies, including
Ecuador, using annual data. Results confirm the overall
usefulness of the Harberger model. Unlike the study at hand,
Vogel utilizes a sample period during which inflation averaged
less than 4 percent per year in Ecuador and the exchange rate
Following numerous applied econometric studies utilizing
this approach, it became apparent that its reliance on
domestic variables alone often provided unsatisfactory
results. Bomberger and Makinen (1979) provide a thorough
examination of the Harberger model using quarterly data for
Korea, Taiwan, and Vietnam. Extensive testing is conducted
using quarterly data in order to establish whether a suitable
characterization of inflation is provided. Encouragingly, the
parameter estimates do not appear sensitive to the time period
selected. However, the elasticities with respect to money and
real income are not always unitary as hypothesized. Also, the
coefficient signs for the cost of holding money variables are
Hanson (1985) extends the Harberger framework in a
systematic fashion to incorporate an important missing
component, import costs. An implicit cost function is
utilized to derive an aggregate supply curve which includes
local prices of imported inputs. When the underlying
production function is homogeneous of degree one, inflation
becomes a weighted sum of money supply changes and import
prices. This is important for studies using higher frequency
data if the problem of measurement bias engendered by
interpolated values of real output, generally published on
either a quarterly or annual basis in developing countries, is
to be avoided (see Bomberger and Makinen, 1979). The model
also implies the elasticity of inflation with respect to money
growth is less than one. Empirical results in the Hanson
article strongly support the inclusion of import prices or the
rate of devaluation in models of inflation.
Subsequent research has provided additional evidence in
favor of the augmented Harberger-Hanson approach wherein the
effect of import prices on inflation is considered. Koch,
Rosensweig, and Witt (1988) and Fullerton, Hirth, and Smith
(1991) both report positive linkages between the trade-
weighted exchange value of the dollar and consumer prices in
the United States. These empirical studies indicate a
unidirectional channel of influence from the exchange rate to
domestic prices exists in the United States economy. As will
be discussed below, causality direction has important
implications for both model form and estimation technique.
Developing country studies have also confirmed the
usefulness of an augmented modeling treatment of inflationary
dynamics. Sheehey (1976) reports some of the early
econometric work along these lines. Sheehey (1980) reaches
additional conclusions on the basis of empirical tests that
indicate that accurate assessment of austerity policy efforts
will likely require explanatory variables representing cost
push factors. More recently, Brajer (1992) provides evidence
that the latter category of models may offer better
specifications than those which rely solely on domestic
economic factors. Conclusions in that article are reached on
the basis of F-tests for different regressor sets. Similarly,
Fullerton (1993b) successfully imbeds a variant of this
approach in a large-scale macroeconometric forecasting model
for Ecuador using annual data.
There have been very few dynamic models estimated on the
basis of monthly data for developing economies. Given that
most business decisions in highly inflationary countries are
reached within a short-range context, this is an area which
needs to be addressed. As detailed in Chapter 2, Fullerton
(1993c) empirically examines Colombian anti-inflationary
efforts utilizing monthly data with an ARIMA transfer
function. The estimated model is found to generate realistic
simulation scenarios for policy analysis. The results also
support the hypothesis of inflation rate inelasticity with
respect to monetary growth. As in the Chilean equation
reported by Hanson (1985), and the Argentine model presented
in Sheehey (1976), exchange rate price effects are found to
outweigh the monetary coefficient. The latter is somewhat
surprising given that imported goods and services comprise
less than 20 percent of Colombian gross domestic product.
3.3 Theoretical Model
Harberger's (1963) model is based on the traditional
quantity theory of money equation:
(3.1) MV = PQ,
where M represents some measure of the money stock, V is the
velocity of circulation, P is the price level, and Q is real
output. Velocity is not assumed to be constant. Instead, it
is hypothesized to be a predictable function of other
macroeconomic variables such as the cost of holding cash
balances. Given the typical variability of velocity in many
Latin American economies, this aspect of the theoretical model
is potentially important (see Clavijo, 1987).
To utilize percentage changes, the variables can be
transformed by natural logarithms and first differenced.
Introduction of a time subscript, and rearrangement of the
terms, yields the basic Harberger equation:
(3.2) DPt = DMt DQt + D(DPt.1),
where the last term results from substituting for velocity and
D represents a difference or backshift lag operator. Usage of
the lagged change in the inflation rate to proxy for the
implicit cost of holding money is motivated by the fact that
developing countries such as Ecuador have frequently imposed
government regulations on interest rates. The latter have
occasionally caused savings and loan rates to become negative
in real terms. Unadjusted interest rates from these periods
in Ecuadorian economic history do not, therefore, provide
accurate estimates for the cost of holding idle cash balances.
Equation 3.2 implies that inflation will vary positively
with the money supply and inversely with respect to real
output. A statistically significant intercept term will enter
the estimated equation if there is a trend in the velocity of
circulation. If only contemporaneous lags of M and Q enter in
the equation, the parameters for both variables are
hypothesized to be unitary. This can be tested empirically
with the following specification:
(3.3) DPt = ao + a1DMt a2DQt + a3D(DPt.1) + u3,
where al and a3 are hypothesized to be positive, and the
absolute values of al and a2 should both be statistically
indistinguishable from one. The last argument in the
expression represents the disturbance term.
Hanson (1985) proposes an implicit cost function dual of
an aggregate production function which is homogeneous of
degree one. Derived output supply functions from this
framework will be homogeneous of degree zero in input and
output prices. Equation 3.4 expresses this relationship using
logarithmic first differences:
(3.4) DQt = b0 + bIDPt b2DPIt + u4,
where PI represents imported input prices. When the relative
prices of imported inputs increase, output is assumed to
decline. The standard homogeneity assumptions for production
and derived supply relations imply that bI b2 = 0.
Equation 3.4 can be substituted into Equation 3.3 to
eliminate the output term from the expression to be estimated.
As noted in the literature review, this step is useful for
avoiding interpolation bias in empirical studies of monthly
inflation for countries such as Ecuador where GDP is published
at quarterly and/or annual frequencies. The resulting
equation can be written as follows:
(3.5) (1 + a2bl)DPt = a0 a2b0 + a1DMt + a2b2DPIt +
a3D(DPt.1) + u5.
Equation 3.5 can be further simplified prior to
estimation. Dividing through by the left-hand side constant
term and rearranging terms such that the price series remains
as the dependent variable yields the following relation:
(3.6) DPt = co + cDMt + c2DPIt + c3D(DPt.1) + u6,
which also has testable properties. Importantly, the
coefficient on the monetary variable, cl, is now hypothesized
to be significantly less than one. Also important, with the
possible exception of the intercept, all of the regression
parameters in Equation 6 are expected to be positive.
Several other properties of this model are worth noting.
In particular, the theoretical coefficient restrictions
described earlier have interesting implications. Namely, a,
and a2 are hypothesized as equal to one, and bI and b2 are
equal in absolute value in the version of the model developed
thus far. Substitution into Equation 3.5 implies cI + c2 = ,
which can also be tested.
As indicated in the literature review, Equation 3.6 has
provided a useful framework for analyzing quarterly and annual
inflation rates. But because the lag structure in this model
is fairly short, it may require additional modification prior
to estimation. This possibility does not reflect any
deficiencies in the theoretical model as such, but arises due
to the fact that short-term models rely upon monthly data. As
a result, if the inflationary impact of a change in the money
supply is felt over the course of one calendar year, the
implied lag structure for a model estimated with data
published at a monthly frequency would potentially range up to
12-months in length. Equation 3.7 takes into account this
empirical issue which has confronted and confounded
researchers for many years (see Laidler, 1993):
(3.7) DPt = co + c1DMti + c2DPIt.j + c3D(DPt.l.k) + u7,
where lag subscripts i, j, k = 0, ..., n, respectively.
The above model provides an attractive starting point for
examining inflationary trends in an economy. It is not,
however, without potential problems for analyzing price
movements in a relatively high inflation country such as
Ecuador. A principal concern arises from the fact that
Equation 3.7 treats all of the regressors as exogenous or pre-
determined. In doing so, it does not allow for the
possibility of statistical feedback or endogeneity between the
left-hand and right-hand side variables.
If a central bank yields to political pressures and
engages in accommodative monetary policy in the face of
inflation shocks, this assumption would be violated. As noted
in Chapter 2, research conducted using higher frequency data
for Colombia indicates that the causality paths in that
economy are unidirectional as implied by Equation 3.7 (see
Fullerton, 1993c, and Leiderman, 1984). While short-term
forecasting models for Ecuadorian inflation have not been
previously developed, domestic prices, monetary aggregates,
and import prices are modeled simultaneously in the
macroeconometric model estimated using annual data by
Fullerton (1993b). It would not be surprising if the feedback
relations encountered in that paper also emerge in the monthly
time series utilized below. As noted elsewhere, monetary
authorities in Ecuador have occasionally been forced to yield
to political pressures (Garlow, 1993). Granger causality
tests will be used to test the severity of this potential
A second possible concern arises from utilizing first
differenced, log-transformed time series data in the equation
to be estimated. If the resulting series are stationary, the
equation can be estimated without risk of obtaining spurious
correlations in the results. As shown in many studies of
hyperinflationary economies, however, higher order
differencing may be required to induce stationarity during
periods in which prices increase rapidly (Engsted, 1993).
Because Ecuador has not undergone any hyperinflationary
episodes, first differencing should remove nonstationary
trends from the variables in question but this assumption must
be tested. The latter tests are accomplished below via a
battery of unit root tests, not all of which are reported.
A third concern arises from the fact that monthly import
price deflators do not exist for Ecuador. To circumvent this
problem and also avoid interpolation bias, a trade-weighted
exchange rate index is used as a proxy for imported input
prices. The index utilized was developed econometrically and
takes into account export and import volume changes with
Ecuador's major trading partners. It also offers a single
monthly index for periods when the government has instituted
multiple exchange rate systems (Fullerton, 1989b).
To construct the currency index, individual currency
weights are calculated as the sum of imports and exports with
each of ten major trading partners and divided by total
international trade in each year. Over the sample period,
annual trade with Ecuador's top ten import sources and export
destinations accounts for more than 70 percent of its total
trade volume in any given year. Products of the bilateral
trade coefficients and the respective currencies are then used
to construct the exchange rate index using a geometric mean.
The latter method is selected to avoid problems which can
potentially result for indexes constructed using arithmetic
means during periods of inflationary variability (see Batten
and Belongia, 1986, Dutton and Grennes, 1987, and Kercheval,
For periods when multiple exchange rates were instituted,
a blended index is calculated. Weights for the free-market
and official government intervention exchange rates are
obtained from the Central Bank publication, Inforuacift
Estadistica Mensual (various issues). Econometric results for
total and disaggregated imports, and non-petroleum exports
indicate that the blended rate provides a more accurate
measure of the appropriate currency basket for Ecuador
(Fullerton, 1989b). Diagnostic tests were also conducted
using currency baskets with different numbers of trading
Introduction of the trade-weighted exchange rate index to
Equation 3.7 causes the model to be estimated to take the
(3.8) DPt = go + g1DMt-i + g2DTWXt- + g3D(DPt-j-) + u8,
where TWX stands for logarithmic first differences of the
nominal version of the monthly trade-weighted exchange rate
index calculated for the sucre by The WEFA Group (formerly
Wharton Econometrics). Although incorporation of the monthly
exchange rate index avoids the problems associated with
implicit price deflator interpolation, it may introduce other
problems due to the fact that import prices in Ecuador are
affected by global supply and international demand conditions
in addition to exchange rate movements (see Fullerton 1993b).
A fourth observation regarding potential pitfalls
associated with the theoretical specification of the model is
worth noting. Only one class of factor input prices, that for
imports, is included. While this represents an improvement
over the original Harberger framework, it may overlook
additional important inputs such as labor. If the version of
the model developed herein omits relevant variables to the
inflationary process in Ecuador, it is likely that estimated
residuals associated with the empirical version will not be
randomly distributed. If this is the case, then correction
for serial correlation will be necessary. Extension of the
model to overcome this problem cannot currently be
accomplished due to data availability.
3.4 Estimation Results
In order to examine whether the working series included
in Equation 3.8 are stationary, unit root tests are conducted
for each series utilized in the model. Estimation is
conducted for the 1964-1994 sample period for which data are
available. Applying unit-root tests to what could be
considered a relatively short time span may be risky due to
the fact that these tests typically have low power unless
long-run data sets are used (Hakkio and Rush, 1991). Because
time series data in Latin America generally date back to 1957
at most, there is little that can be done to circumvent this
Augmented Dickey-Fuller t-statistics are estimated for
equations with both intercepts and trends. These results,
compared against the corresponding MacKinnon critical value,
appear in Table 3.1. In all cases, tests for unit roots in
the first differenced log transformed series for consumer
prices, money, and the trade-weighted exchange rate index are
rejected at the 1-percent level. Based on this evidence, the
first-order differenced series used to estimate Equation 3.8
are assumed to be stationary.
As specified, the model is explicitly built around a set
of unidirectional causality relations from movements in the
regressors to consumer prices. To examine whether the absence
of simultaneity in the model is plausible, a set of Granger
causality tests are calculated for the stationary components
of the series of interest. These results are reported in
Table 3.2 for lags of 6, 12, 18, and 24 months. Tests are
conducted for prices and the money supply, as well as prices
and the trade-weighted exchange rate.
Similar to the Colombian results reported in Chapter 2,
movements in M1 do not appear to be systematically preceded by
changes in the CPI in a statistically significant manner at
the 5-percent level. Essentially, this implies that monetary
policy in Ecuador is conducted in a manner that is not
accommodative of price shocks. Although central bank linkages
to the executive branch of the government are relatively
strong, steps have been taken in recent years to increase both
monetary policy autonomy and currency stability (Banco del
Implications based on the Granger causality tests
estimated for consumer prices and the exchange rate series are
less clear. At shorter lag lengths, the null hypothesis that
changes in consumer prices do not lead to subsequent changes
in the international value of the sucre is rejected at the 5-
percent level. This conclusion, similar to the Colombian
results obtained by Fullerton (1993c) and Kamas (1995), is not
upheld at longer lag lengths and makes it difficult to reach
concrete conclusions regarding potential feedback effects
between the two series. However, truncation of the longer lag
lengths can bias the hypothesis toward incorrect rejection of
the null hypothesis (Feige and Pearce, 1979). Consequently,
Table 3.1: Unit Root Tests for Stationarity
Series Aug Dickey-Fuller t-stat MacKinnon crit value
P 9.778 (with const, trend, 1 lag) -3.995 (1% ivl)
M -16.626 (with const, trend, 1 lag)
TWX -13.652 (with const, trend, 1 lag)
Table 3.2: Granger Causality Tests
Causality Direction Number of Lags Computed F-stat
CPI => M1 6 1.659
CPI => Ml 12 1.309
CPI => M1 18 1.420
CPI => M1 24 1.509
CPI => TWX 6 2.900
CPI => TWX 12 2.214
CPI => TWX 18 1.664
CPI => TWX 24 1.029
a unidirectional channel of influence from exchange rate
movements to prices appears to be a reasonable assumption.
Given the latter, model estimation is conducted without
resorting to instrumental variables, or developing a system of
simultaneous equations, and the resulting coefficients are
assumed to be unbiased and consistent.
Regression results for Equation 3.8 are summarized here:
(3.9) DPt = 0.015 + 0.013*DMt +
0.028*DMt.4 + 0.039*DMt.9 +
0.060*DTWXt + 0.007*DTWXt.2 +
0.063*D(DPt.6) + 0.405*Ut2 +
R2 0.379 S.E.R. 0.015 Log likelihood 1013.715
DW 2.087 F-stat 26.798 Prob(F-stat) 0.001,
where the numbers in parentheses are computed t-statistics and
Vt is the error term associated with the ARMAX model for Ut.
Lag lengths of 24 months were used in the initial estimates
for Equation 3.9. While a large number of the resulting
coefficients were significant at the 5-percent level, serial
correlation was present in the residuals. To avoid
potentially spurious estimation results (see Hamilton, 1994),
a nonlinear ARMAX procedure is utilized to correct for
autocorrelation. This estimator (Pagan, 1974) is useful
because of its flexibility in handling a variety of different
error generating processes.
Correcting for serially correlated disturbances caused
the computed t-statistics for many coefficients to become
insignificant at the 5-percent level. Because inclusion and
exclusion of numerous different lags did not yield clear
results, the model structure reported in Equation 3.9 was
selected on the basis of likelihood ratio tests. Although the
relatively short lag components may seem unexpected, they were
confirmed by cross correlation function analysis. To handle
autocorrelation, an ARMA(2,1) specification is used to
characterize the data generating process for the residuals.
As would be expected in an inflationary economy, the
algebraic sign of the intercept in Equation 3.9 is positive.
Because the model is estimated using differenced data, this
result indicates that a systematic upward trend exists in the
Ecuadorian consumer price index. As hypothesized by Hanson
(1985), the sum of the coefficients for the lagged monetary
series is significantly less than one. Similar to Fullerton
(1993a, 1993c), but unlike Kamas (1995), the exchange rate
appears to play an important role in determining price
movements. The coefficient for the velocity of money supply
circulation proxy was not, however, significant at the 5-
Although the sum of the lagged monetary aggregate
parameters is less than one, the sum of those coefficients
with the exchange rate variable coefficients does not equal
one as implied by the reduced form of the theoretical model
specification. These results cast doubt upon the relatively
simple version of the Harberger-Hanson framework developed
above. As noted previously, this is not completely unexpected
and is a potential root cause underlying the presence of
autocorrelated residuals in the initial empirical results. To
see if the theoretical model can be improved, an alternative
version of the approach is currently under development for an
economy where data shortages are less severe and a richer
input structure can be handled (Kim and Fullerton, 1996). The
latter effort will also benefit from the incorporation of an
interest rate variable to measure the cost of holding idle
cash balances, as well as an import price index rather than
the exchange rate proxy utilized herein.
In 1983, the Central Bank introduced new exchange rate
policies that allowed the sucre to fluctuate more freely. To
allow for potential parameter heterogeneity caused by
structural change associated with periodic currency
devaluations, a shorter sample was also used for estimation
purposes. Results from these exercises, not reported here,
generally support the empirical estimates presented above.
Experimentation with the lag structure over the shorter sample
period does not yield strong evidence of parameter instability
or any other major shortcomings with Equation 3.9.
3.5 Policy Simulation Results
From the alternative specification and sample size
results, Equation 3.9 does not seem overly fragile.
Alternative lag structures and ARMAX processes were compared
to the model using likelihood ratio tests to evaluate the
results. While it was not possible to reject the
specification shown above, it does seem likely, however, that
additional time series analysis will be required in order
reach firm conclusions regarding inflationary dynamics in
Ecuador. For that reason, the modeling and simulation results
associated with Equation 3.9 should be regarded as preliminary
in nature. They do, however, provide a good starting point
for understanding short-term Ecuadorian price movements and
assessing government policy objectives.
Cognizant of the paucity of comparative research results
in this segment of the literature, a variety of simulation
experiments are conducted using Equation 3.9. The goal of the
simulation tests is to shed light on the feasibility of
attaining the inflationary stabilization targets announced by
the Durdn Ballen-Dahik Garzozi administration in 1995. Sample
data used to estimate Equation 3.9 only includes information
available to government policy makers at the time the
inflation target was announced. The simulation analyses thus
satisfy the Klein (1984) and Christ (1993) criteria for
forecast evaluation. Of course, additional policy analysis
with different model specifications may also be useful.
To examine the feasibility of the government's inflation
goals, four simulation exercises are conducted using Equation
3.9. The first exercise assumes immediate implementation of
price stabilization plan wherein the annualized rate of growth
in the money supply and the rate of devaluation are reduced to
20 percent within one month of the policy announcement.
Scenario two examines rapid implementation of the price
stabilization whereby growth in the money supply and the rate
of devaluation are reduced to 20 percent over a 6-month phase-
in period. The third simulation test analyzes the effects of
gradual introduction of the anti-inflationary program with
money supply growth and the rate of devaluation lowered to 20
percent during a 12-month period. The rates of money supply
growth and currency depreciation reflect the revised policy
targets adopted following the border conflict with Peru and
the aftermath of the "Tequila effect" associated with Mexico's
December 1994 peso devaluation (Banco del Pacifico, 1995).
Policy simulation results are reported in Table 3.3. The
impacts associated with all three implementation scenarios are
striking. In each, reducing the rate of money supply growth
and slowing the rate of nominal currency depreciation to 20
percent over a one-to-twelve month implementation period
causes inflation to decline to less than 25 percent. This is
somewhat close to the policy target established by the
government in late 1994. If implementation of the
stabilization program is immediate, annual consumer prices
increases fall to slightly more than 23 percent. In a more
likely scenario under which intermediate policy targets are
attained more gradually, disinflation is still fairly rapid
and overall policy credibility would not appear to be at risk.
To actually achieve the announced inflation target, however,
does not appear likely.
The final column of Table 3.3 contains the results
associated with a scenario in which no progress is made with
respect to lowering the rate of growth in the money stock.
Similarly, nominal depreciation of the sucre is not lowered
under this simulation. In contrast to the "no implementation"
policy simulation results reported in Table 2.7, the inflation
rate remains practically unchanged if no intermediate steps
are taken by Ecuadorian monetary authorities. This result is
due in large part to the fact that consumer prices in Ecuador
rose on average by approximately 26.7 percent per year during
the past three decades. As a result, the policy experiments
illustrated in Table 3.3 have a common starting point that is
almost identical to the sample period mean.
On the basis of the empirical evidence obtained in this
chapter, it appears that Ecuador's anti-inflationary program
is fairly credible. This conclusion is predicated upon
eventual deceleration in the rates of liquidity growth and
currency depreciation brought about by the central bank. In
light of previous monetary policy analysis conducted for
Table 3.3: Policy Simulation Results
Month Immediate Rapid Gradual None
1 26.1 26.2 26.2 26.2
3 24.9 25.1 25.3 26.0
6 24.3 24.8 25.1 25.8
9 24.0 24.5 25.1 25.9
12 23.2 23.7 24.2 26.0
Ecuador, the results obtained are pleasantly surprising
(Fullerton, 1990a, offers a negative assessment of an earlier
price stabilization effort). That the government's announced
targets are not completely attained is in line with other
studies of Latin American inflation policies (Fullerton,
1993c, and Kamas, 1995).
An empirical model of Ecuadorian consumer price inflation
is developed and estimated in this chapter by incorporating
both monetary and import cost effects in a theoretically
plausible manner. Specification and simulation of the model
are relatively easy to accomplish. Experimentation with the
estimated equation indicates that the current anti-
inflationary goals of Ecuadorian monetary authorities are in
large part attainable. Because the model does not pose
stringent data requirements, it may be applicable to other
Latin American economies where inflation remains a problem.
Examples include Brazil, Colombia, Mexico, and Venezuela where
authorities continue to grapple with short-term price
Additional econometric testing should prove useful.
Initial results reported above indicate this framework will
likely benefit from incorporating a more realistic conceptual
model. Because ARMAX treatment of nonrandom movement in the
initial model residuals was necessary, expansion of the scope
of the model to include additional factors such as labor costs
may be required to more completely specify the inflationary
process in Latin America. Doing so, however, may necessitate
the usage of instrumental variables or the introduction of
multiple equations which allow for potential endogeneity
between prices, money, import costs, and wage rates. Even if
the latter are not required for parameter estimation
consistency, they could enrich subsequent policy simulation
These suggested changes represent avenues for refinement
to the basic model outlined above. They are not likely to
result in wholesale alterations to the general framework.
Similarly, it is not clear that policy simulation impacts and
conclusions will change markedly due to expanding the scope of
the empirical techniques presented above. But given the
breadth of economic conditions prevailing across Latin
America, steps in these directions may prove helpful to
subsequent econometric research of this nature. Given the
divergence between the theoretical model parameters and the
estimated coefficients, additional empirical testing is
PREDICTABILITY OF SECONDARY MARKET DEBT PRICES
While inflation has undoubtedly been one of the most
hotly debated items in Latin American policy debates, issues
related to external obligations have also been important
topics of discussion in the region. Following the outbreak of
the international debt crisis in September 1982, a secondary
market for sovereign debt instruments became active in the
major world financial centers. As the payments crisis spread
from Mexico to the rest of Latin America, much of Africa, and
parts of Asia, secondary market trades in developing country
sovereign debt paper increased. Key issues in the emerging
debate regarding potential solutions to the payments problem
often involved the treatment of discounts from face value
implied by secondary market debt prices.
Accordingly, researchers began investigating different
aspects of the behavior of secondary market developing country
debt prices. Much of this work investigates the applicability
of theoretical valuation models to assessing implied discounts
from face. Other research has used secondary market prices
and other financial data to evaluate the probability of
payment rescheduling requests. Authors have also attempted to
assess the sensitivity of the secondary market to
macroeconomic fundamentals, but these efforts have not
investigated whether or not the discounts from face value on
these obligations are predictable.
Several basic questions are investigated in the chapter
at hand. An important issue to be considered is the nature of
the time series behavior of these prices. Under certain
conditions in an efficient market, each individual series
might be expected to follow a random walk. Of course, in a
relatively thin secondary market such as that for developing
country debt, the perfectly competitive hypothesis may not
always be satisfied. If movements in the price series are
nonrandom, it may be possible to relate these variations to
changes in other domestic and international economic
indicators which are generally included in commercial
forecasts of the region. Candidate series which help assess
creditworthiness include variables such as interest rates,
export prices, and international reserves (see Fullerton,
Anecdotal evidence indicates that countries with good
payments records such as Colombia have seen their access to
international commercial credit diminish over the last fifteen
years. If the "good debtor in a bad neighborhood" contagion
effect is present, this will be manifested in the movements of
the discounted prices. Debt prices for individual countries
would consequently be affected by developments in neighboring
countries and correlated with the prices for the instruments
of those economies and the market in general. The generalized
least squares estimation procedure used below implicitly takes
this possibility into account by allowing for
contemporaneously correlated residuals across equations.
To examine the predictability of these series, forecasts
are generated using jointly estimated individual country
models. For comparison purposes, baseline projections are
developed using the random walk assumption that the best
forecast is one of no deviation from the last observation
available in each data set. Modified Theil U-coefficients are
then calculated on the basis of root mean squared error (RMSE)
ratios estimated for each set of model based and random walk
forecasts. The U-statistic for an individual forecast step-
length is equal to the ratio of the model based RMSE to that
of a no change RMSE. When the resulting coefficient is less
than one, the equation forecast has outperformed the random
4.2 Earlier Studies
There have been a growing number of studies regarding
developing economy external indebtedness and the secondary
market in recent years. Gennotte, Kharas, and Sadeq (1987)
develop a numerical debt valuation method based on a financial
options pricing technique. Using liquid international
reserves plus the estimated values of the capital stocks in
mining and manufacturing as proxies for collateral in each
country, theoretical values for developing country foreign
debts are simulated under different scenarios involving
interest rate changes, principal due, and front-end fees.
Simulation results are found to be positively correlated with
secondary market prices reported for 1985.
Stone (1991) examines the behavior of implied returns on
secondary market sovereign debt instruments. Using an
arbitrage pricing model approach, he examines the empirical
relationships between secondary market returns and various
macroeconomic variables. To control for cross equation
disturbance simultaneity, a seemingly unrelated regression
estimator is utilized. Poor equation fits and weak t-
statistics indicate that movements in implied sovereign debt
returns are not readily explained by arbitrage pricing
Other empirical studies of sovereign debt problems have
been more successful. Rahnama-Moghadam and Samavati (1991)
employ probit models to examine the propensity to default.
Ten different macroeconomic and international financial ratios
are used to quantify the probability of rescheduling. Among
the ratios found most useful in predicting debt moratoria,
formal or informal, are the following: international reserves
relative to imports of goods and services; international
reserves to disbursed debt; disbursed debt relative to
exports; disbursed debt to gross domestic product; and
interest payments relative to exports of goods and services,
also known as the interest service ratio. Parameter estimates
are based on annual cross country sample data from around the
The probit results discussed in the preceding paragraph
were later confirmed by subsequent research which utilized
data from Latin American economies (Rahnama-Moghadam,
Samavati, and Haber, 1991). Reasons offered for segmenting
the data in this fashion include geographic, structural, and
institutional similarities among countries in Latin America.
The authors also point out that economies in this smaller
sample are all middle-income countries which share similar
characteristics in terms of overall development. The models
exhibit relatively high goodness-of-fit statistics, individual
coefficients with expected algebraic signs, statistically
significant parameter estimates, and coefficient stability
across different specifications. As debt problems arise from
a number of different determinants, it would appear that a
further refinement from a regional focus to that of an
individual economy may be useful.
The roles of fundamental economic and financial factors
in the evolution of secondary market sovereign debt prices
have been directly examined in recent research. As shown by
Anayiotos and de Pinies (1990), these types of
characterizations are straightforward and intuitively
appealing. In a statistical framework, variables selected to
capture market fundamentals can also be combined with
regressors designed to represent exogenous risks. Using
pooled observations and annual data, the econometric results
of these authors show that even simple specifications can
represent secondary market developing debt prices with a high
degree of accuracy.
Perasso (1989) also emphasizes economic factors in his
study of secondary market prices. To reflect the importance
of debt-equity conversions, his pricing model is derived from
a profit maximizing framework that includes real interest rate
measures, real costs of capital, international wage
differentials (assumed to induce manufacturers to invest
abroad), and individual economy performance variables. Time
series and cross country annual data are pooled prior to
estimation. Some coefficients are statistically insignificant
or of the wrong sign, but overall empirical results for the
estimated equations for secondary market prices are fairly
Empirical models developed by investment bankers indicate
that secondary market debt prices can be modeled and
forecasted (see Purcell and Orlanski, 1988, 1989). Similar to
the studies mentioned above, these models also rely upon
pooled cross section time series data for different countries.
Regressors used to estimate equation parameters include debt
to export ratios and per capita incomes. Dummy variables for
payment rescheduling programs, principal payment moratoria,
and debt retirement agreements are also entered as right-hand
side variables. Model simulations using individual country
data are used to calculate specific secondary price forecasts.
As mentioned previously, not all of the statistical
evidence with respect to the behavior of secondary market
prices leads to the same conclusions. Laney (1987) concludes
that economic factors are more important as explanatory
variables than political and structural risk factors. Sachs
and Huizinga (1987) report regression results that indicate
both economic and political variables have key roles to play
in modeling developing country debt discounts. Similar to
other studies, the latter also utilize pooled cross country
data in calculating equation parameters.
4.3 Empirical Analysis
As the literature review indicates, there have been a
variety of studies with interesting econometric results
published in recent years. However, none of the initial
efforts attempted to model secondary market debt prices for
individual countries using time series sample observations.
Given the differing sources of debt servicing difficulties, a
country by country examination of secondary market price
movements appears warranted. Similarly, individual
forecasting models or equations have not been systematically
tested to examine whether debt prices can be predicted with
any degree of accuracy. Despite the fact that financial
market participants focus almost exclusively on short-term
movements in sovereign debt prices, earlier research efforts
also failed to study high-frequency data from this market.
In this section of the chapter, simple forecasting models
are proposed, estimated, and simulated for three individual
debtor countries. These equations are modeled jointly using
monthly data series. As stated in the introduction, modified
Theil inequality coefficients are calculated using random walk
forecasts as the benchmarks to which the model projections are
compared. Although the individual equation specifications
employed are straightforward, recent financial market research
underscores the potential success of simple forecasting models
(see Granger, 1992, and Christ, 1993). This initial attempt
provides a useful starting point for addressing questions
regarding the predictability of secondary market developing
country debt prices.
Debt price data used in the empirical estimates are
collected by Salomon Brothers in New York, with monthly
averages published by The WEFA Group in Philadelphia. The
sample period is March 1986 December 1991. Countries
included in the sample are Colombia, Ecuador, and Venezuela.
These countries have interesting and differing histories with
respect to their external debt management practices and their
individual approaches to economic policymaking in general.
Colombian debt management practices have traditionally
been more conservative than those of either of its neighbors.
Government economists have consistently treated international
credit markets as sources of financing to bridge domestic
savings gaps (Fullerton, 1990b). Colombian debt negotiators
have never sought a Brady initiative write-off, arguing that
to do so would only impair the nation's creditworthiness.
Over the period from 1945 forward, Colombia has never declared
an interest payment moratorium and has one of the best debt
service records among developing countries which have utilized
external financing sources.
Ecuador declared principal and interest moratoria in 1987
as a result of the financial aftermath following the
earthquake which shattered the country's transAndean oil
pipeline. The latter event interrupted Ecuador's principal
source of export earnings and destroyed much of its physical
infrastructure. In 1989, the Borja administration resumed
negotiations with commercial creditors and eventually began
honoring 30 percent of the interest coming due on commercial
loans (Fullerton, 1989c). Progress regarding the treatment of
growing amortization and interest arrears remained elusive
through the balance of the Borja government which stepped down
in 1992. A new round of discussions with Ecuador's bank
advisory committee began after the Duran Ballen-Dahik Garzozi
government took office.
Venezuela also encountered debt service problems
following negative oil price shocks in 1986 and 1988.
Eventually, the Perez administration rescheduled commercial
credits under a Brady initiative agreement with Venezuela's
bank advisory committee (Fullerton, 1990c). The agreement
offered bank loan syndicate members five menu options designed
to relieve balance of payment pressures faced by this economy.
Several Euromoney bond issues were successfully floated in
subsequent periods and Venezuela temporarily regained access
to international credit markets.
Debt instruments for all three countries have traded at
substantial discounts from face value in recent years. Given
its superior service record, it is not surprising that
Colombian paper generally carries a higher price than that of
its two Andean neighbors. Similarly, given its higher level
of payment arrears and worse economic performance, Ecuadorian
paper tends to trade at sharper discounts than those of the
other countries in the sample.
Table 4.1 presents summary statistics for each secondary
market sovereign debt price series. Discounts from face value
on Colombian debt certificates were less variable than those
of Ecuador and Venezuela during the March 1986 December 1991
sample period. The range and standard deviation for Colombian
debt quotes are smaller than the others, while those for
Ecuador are the largest of the three. The arithmetic means
for each series follow the opposite pattern in terms of
ranking. Data in Table 4.1 are in cents per dollar, or
percent of face value of the loans, the units in which
transactions are conducted at money center trading desks.
Table 4.1: Secondary Market
Country Average Maximum
Colombia 69.990 86.00
Ecuador 30.332 68.00
Venezuela 57.635 78.50
The sample period is March
Debt Price Summary Statistics
Minimum Standard Deviation
L986 December 1991.
Each series is modeled as a function of key economic and
financial variables which are easily observed as well as
likely to be used by secondary market participants as
indicators of the creditworthiness of the individual country.
Possible regressors include lagged debt quotes, world interest
rates, commodity export prices, international reserves, and
domestic price indexes (for discussion, see Wakeman-Linn,
1991). As mentioned above, series such as these are typically
included in macroeconometric forecasting models of Latin
America due to their usefulness in predicting balance of
payment movements and general economic performance (see
Fullerton, 1993a, 1993b).
A three-stage generalized least squares (3SLS) regression
technique is used to jointly estimate model parameters
(Zellner and Theil, 1962). Doing so permits incorporating
potential cross-equation simultaneity effects of events such
as Citibank's decision to unilaterally increase loan loss
reserves in 1987. The latter is believed to have reduced
overall secondary market liquidity and also reduced the
attractiveness sovereign debt paper to most creditors,
irrespective of their individual payment records (Gajdeczka
and Stone, 1990, and Snowden, 1989). This estimator also
allows for potential simultaneity between the dependent
variables and the independent variables. In the case of
Ecuador, this is important because feedback exists between the
secondary market debt price and the 180-day LIBOR.
Individual models may be written conceptually as:
4.1 Pt = b0 + blxlt + ... + bnxnt + et,
where Pt is the secondary market sovereign debt price series
for an individual country at time period t, x1t, ..., Xnt are
predetermined domestic and international variables for each
economy, et is a random disturbance term, and bl, ..., bn are
regression coefficients. From a theoretical modeling
perspective, this approach may seem informal. Two points are
First, alternative methodologies were originally
considered but deemed inappropriate due to data requirements
and difficulties in applying them to forecasting problems
(Fullerton, 1990d). Second, the complete absence of other
studies of this nature increases the value of an initial
attempt to establish whether any regularities at all are
present in the data (Christ, 1994). Both points were
repeatedly raised by financial economists who participate in
the secondary market and attended the Sovereign Debt
Conference sponsored by The WEFA Group in New York in 1990.
Similar observations are also made by Friscia (1993).
As mentioned above, parameter estimation is accomplished
using the 3SLS methodology developed by Zellner and Theil
(1962). Other procedures were considered, but 70 monthly
observations constitutes a fairly small sample for many time
series estimators. The series are not differenced prior to
estimation, but are logarithmically transformed. Because the
data are in levels, it is important to assess whether the
series are cointegrated. The latter assumption is tested via
unit-root tests on the individual model residuals (for
discussion, see Hamilton, 1994).
Because monthly data are used, it is not possible to
utilize the same regressor variables as have been used in
earlier studies incorporating quarterly or annual series from
national income and product accounts. There are still a
number of potential candidate series which the financial
community may use as indicators of a country's
creditworthiness and will potentially influence secondary
market price quotes. It should be noted that sets of
indicators will vary for different countries according to
individual economic endowments and performance records. This
argument is similar to that previously made for equations used
to estimate developing country borrowing levels under
different regimes (Eaton and Gersovitz, 1981).
For Colombia, Equation 4.2 in Table 4.2, the regressors
include a one-month lag of the debt price, the effective
annual rate for the 180-day London interbank offer rate
(LIBOR), and the one month change in the national consumer
price index (CPI). The one-month lag on the debt price is
included to provide information on how the market has valued
Colombian paper in the most recent period. International
loans to sovereign nations typically carry variable interest
rates defined in terms of a fixed spread over the 6-month
LIBOR. Upward changes in the variable interest rate assessed
on such loans will reduce Colombia's current account balance
and exert downward pressure on secondary market prices. The
rate of inflation is used as a proxy for overall economic
conditions. When inflation rises, Colombian monetary
authorities generally attempt to tighten credit conditions
(Fullerton, Fainboim, and Agudelo, 1992). Parameter estimates
for each variable have the expected arithmetic signs, but the
t-statistic for the inflation term is not significant at the
In the case of Ecuador, Equation 4.3 in Table 4.2, only
two predetermined variables are included in the three-staged
least squares equation. The first is a one-period lag of the
secondary market debt price series. The second is the 6-month
LIBOR rate. These series are included for the same reasons as
they were used in the Colombian equation. Both coefficients
have the expected signs and are statistically significant.
The absence of other balance of payment indicator series such
as export prices may seem surprising. Because Ecuador,
similar to Colombia, has a relatively diverse commodity export
basket, no single price series will suffice (Fullerton, 1993a,
1993b). This is not the case for Venezuela, where petroleum
products account for more than 80 percent of total merchandise
exports (Fullerton, 1990c).
Table 4.2: Three-Stage Least Squares Regression Results
The sample period is March 1986 December 1991.
Four independent variables are included in the Venezuelan
model. As shown in Equation 4.4 in Table 4.2, they include a
one-period lag of the debt price, the 180-day LIBOR series,
international reserves net of gold, and the average price of
petroleum exports. International reserves and the oil export
price are employed as proxies for expected economic conditions
in Venezuela. Although related, the two series do not always
follow parallel paths. More specifically, the level of
international reserves serves as an indicator of domestic
economic policy success or failure (Fullerton, 1990c). World
oil prices provide a measure of global demand for the nation's
principal export. All of the regressor coefficients have the
expected signs. The t-statistic for international reserves,
however, is not significant at the 5-percent level.
The 3SLS technique utilized to estimate the parameters
reported in Table 4.2 allows for potential cross equation
contemporaneous error correlation, a reasonable assumption for
the secondary market for sovereign debt paper. To examine
whether this assumption is necessary, residuals from the first
stage ordinary least squares (OLS) regression are tested for
contemporaneous correlation using a standard t-test
methodology (see Ostle and Mensing, 1975, or Snedecor, 1956).
As shown in Table 4.3, the OLS residuals for Colombia and
Venezuela are correlated in a statistically significant
manner, as are those for Ecuador and Venezuela.
Table 4.3: OLS Cross Equation Correlation Coefficients
Equation Residuals Correlation Coefficient Computed t-stat
Colombia-Ecuador 0.101 0.772
Colombia-Venezuela 0.340 2.750
Ecuador-Venezuela 0.453 3.872
Unit-root cointegration tests performed on the individual
country 3SLS residuals are reported in Table 4.4. In all
three cases, it appears cointegrating vectors have been
obtained. Note that this procedure may be inappropriate for
the short data set which currently exists for secondary market
developing country debt prices. Previous research has shown
that tests associated with unit-root techniques have low power
when applied to short-run data sets (Hakkio and Rush, 1991).
For purposes of the chapter at hand, the results in Table 4.4
are interpreted as evidence that the 3SLS regression results
are not spurious. Given the diagnostic statistics also
reported in Table 4.2, the latter conclusion is probably
To conduct ex ante dynamic simulation exercises, the
equations were jointly reestimated and simulated for 24
different historical subperiods. Four month ahead forecasts
were produced for each secondary market debt price series from
the last subperiod observation forward. A four-month step
length is sufficient to incorporate quarterly portfolio
accounting considerations faced by international banks. As
pointed out by participants at the 1990 Sovereign Debt
Conference, quarterly corporate income tax filing requirements
often trigger loan certificate swaps between secondary market
participants and make a four-month time frame logical for
Table 4.4: Unit Root Cointegration Tests
Country Aug Dickey-Fuller t-stat MacKinnon crit value
Colombia -4.895 (with const, trend, 1 lag) -4.122 (1% ivl)
Ecuador -5.140 (with const, trend, 1 lag)
Venezuela -5.320 (with const, trend, 1 lag)
To further enhance the realism of the extrapolation
scenarios, actual forecast data available to the financial
markets during each of the 24 subperiods are incorporated in
the simulations. By relying on forecast data which are
unconditional upon any information not available prior to the
start of any simulation period, Klein's (1984) forecast
evaluation criterion is met. These estimates for the
independent regressors were compiled from international
outlook reports published by The WEFA Group from 1989 to 1991.
Forecast results for each price series are summarized in
Table 4.5. For Colombia, the results indicate that the
secondary market LDC debt price series is predictable using
econometric methods. Although the 1-month ahead Colombian
projections have a slightly greater than unity U-coefficient,
the remaining estimates are all less than one. Interestingly,
the Colombian inequality coefficients monotonically decrease
as the length of the forecast period increases. This suggests
that inclusion of econometric information, in this particular
instance, grows in importance as the timeframe under
In the case of Ecuador, the results reported here suggest
that its secondary market debt discount is unpredictable.
Consequently, it appears that financial analysts can do no
better than utilize the last available observation for
Ecuadorian paper in anticipating future quotes. Additional
research with alternative estimators and different
Table 4.5: Modified Theil Inequality Coefficients
Series 1 Step 2 Steps 3 Steps 4 Steps
Colombia 1.070 0.888 0.728 0.611
Ecuador 1.715 2.109 2.408 2.614
Venezuela 1.263 1.207 1.108 1.017
The simulation period is January 1990 December 1991.
specifications might obtain superior forecast accuracy, so it
may be premature to conclude that the Ecuadorian debt price
series is not predictable. As presented above, however, the
results obtained in this chapter provide a fairly striking
example of the fact that a relatively high coefficient of
determination does not guarantee automatic simulation
Forecasting results for the Venezuelan debt price series
are also interesting. Similar to Colombia, the inequality
coefficients decline monotonically as the length of the
projections increases. Unlike the Colombian example, however,
the Venezuelan U-statistics remain at least slightly above
unity at each step length. On the basis of the results in
Table 4.5, it therefore appears that a random walk approach
yields better forecasts. Experimentation with other
estimators is probably warranted. Similar to Ecuador,
however, the Venezuelan modeling and simulation results
provide another example of a case in which a high coefficient
of determination does not guarantee prediction accuracy.
A simple questioned is asked in this chapter. Are
secondary market debt prices predictable? To shed light on
the possible answer, several steps are taken which have not
previously been investigated. A key aspect that distinguishes
this research from earlier efforts is the modeling of monthly
time series data for individual economies, as opposed to cross
section annual or quarterly samples used elsewhere. Also
developed are forecast exercises designed to meet the needs of
participants in international financial markets where
developing country debt instruments are traded.
Modeling and simulation results reported here provide
only limited evidence that it is possible to forecast
secondary market developing country debt discounts from face
value. All three series in the sample exhibited interesting
model characteristics. In particular, the equations and
forecasts for Colombia are encouraging. The same cannot be
said of the Ecuadorian and Venezuelan prediction tests, as in
both cases simple random walk forecasts of the respective debt
price series prove more accurate.
Because the three economies, and their respective debt
price series, differ substantially from one another, it may be
useful to apply this modeling approach to other debt price
series. Obvious candidates include Argentina, Brazil, Chile,
Mexico, and Peru. Further specification enhancements for
Ecuador and Venezuela may also provide useful information.
Inclusion of institutional variables related to Brady-
initiative debt negotiations might prove beneficial,
especially if it is possible to construct monthly indices for
these factors. As noted by Friscia (1993), however,
confidentiality restrictions may preclude this possibility.
Similarly, it may be worthwhile to test alternative
estimators. The latter will become more feasible as
additional observations and information regarding this market
become available. On balance, however, it appears that
movements in secondary market debt prices are not predictable.
SUGGESTIONS FOR FUTURE RESEARCH
In Latin America, short-term econometric forecasting
analysis is still a largely uncharted area of research.
Material presented above indicates that a variety of
techniques, methodologies, and modeling approaches may yield
interesting insights with respect to both forecasting and
policy issues. Not surprisingly, this research has only
hinted at a few of the potentially beneficial topics which
merit further attention.
As discussed elsewhere, the availability of low cost
computer hardware and software, plus the development of wide
coverage high-frequency data banks, will help encourage
additional research of this nature (Fullerton, 1992). With
respect to the results presented above, new efforts are
already underway in terms of alternative estimators for
secondary market debt price forecasting. The underlying
theoretical model presented in Chapter 3 has also been
extended to include labor costs as part of the price vector on
the output side for studying price dynamics.
From a business forecasting perspective, a promising area
of endeavor is likely to arise from the publication of
quarterly national income and product account data. In
countries such as Ecuador, this will permit the development of
large-scale macroeconometric models such as those pioneered by
Barger and Klein (1954). The latter continue to enjoy a
central role in business and government planning exercises
throughout the world. Given the short-term uncertainties
present in Latin America, quarterly modeling and forecasting
will likely be welcomed with enthusiasm. At this juncture,
much work remains to be done, but initial efforts such as
those presented above point to numerous potential successes to
be gained from future research efforts.
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Thomas M. Fullerton, Jr. is a senior economist in the
Forecasting Program of the Bureau of Economic and Business
Research at the University of Florida. Fullerton is co-author
of The Florida Outlook, a quarterly forecast of the state and
20 metropolitan economies. He also teaches a course on Latin
American political economy. Fullerton previously worked at
Wharton Econometrics as international economist in charge of
modeling, forecasting, and policy analysis for Colombia,
Ecuador, and Venezuela. He also worked as an economist in the
Executive Office of the Governor of Idaho where he forecast
the state economy and conducted fiscal policy analysis during
legislative sessions. He began his career in the Planning
Department of El Paso Electric Company. His research has been
published in outlets such as Applied Economics, Journal of
Forecasting, Public Budgeting & Finance, Atlantic Economic
Journal, Journal of Policy Modeling, Business Economics,
Applied Economics Letters, and International Journal of
Forecasting. Fullerton holds degrees from the University of
Texas at El Paso, Iowa State University, and the Wharton
School of the University of Pennsylvania. He is a doctoral
candidate in economics at the University of Florida.
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Carol Taylor West, Chair
Professor of Economics
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
David A. Denslow
Professor of Economics
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Assistant Professor of
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Terry '. McCoy
Professor of Latin American
This dissertation was submitted to the Graduate Faculty
of the Department of Economics in the College of Business
Administration and to the Graduate School and was accepted as
partial fulfillment of the requirements for the degree of
Doctor of Philosophy.
Karen A. Holbrook
Dean, Graduate School
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INGEST IEID EJFCDMDO8_C5CGBK INGEST_TIME 2014-06-17T15:24:45Z PACKAGE AA00022260_00001
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