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STRUCTURAL BREAKS AND TRADE ELASTICITIES IN BRAZIL: A TIME-VARYING COEFFICIENT APPROACH
Angelo Marsiglia Fasolo
ABSTRACT
In this paper, new estimates for trade elasticities in Brazil are presented, using time-varying
parameters techniques based in the Kalman Filter in order to control for structural breaks in
those parameters. Despite major changes in long-run elasticities, there is no evidence of
significant changes in Brazilian exports in the short run. However, some slight signs of
change can be verified in the exports of specific goods where Brazil does have comparative
advantages, such as basic goods. Concerning imports, their most important structural
determinants are the trade liberalization in the early 90’s and the stabilization of the
economy after the Real plan (1994). Tests do not offer conclusive results about the influence
of exchange rate’s volatility and regime in trade, as presented in the recent literature. There is
not support, also, to the “residual exports” hypothesis, in which exports have an inverse
relationship with economic activity.
KEYWORDS: Trade balance, structural breaks, Kalman Filter
Research Department, Central Bank of Brazil. E-mail: [email protected]. The
views expressed in this work are those of the author and do not necessarily reflect those of the Central Bank of Brazil or its members.
STRUCTURAL BREAKS AND TRADE ELASTICITIES IN BRAZIL: A TIME-VARYING COEFFICIENT APPROACH
1. Introduction:
Trade policy, especially in emerging economies, is an important feature in a coherent
economic policy design. Because of the small relative size of their economies, the external
sector equilibrium in these countries is definitely affected by the international environment
and monetary policy’s consequences over exchange rates. The Brazilian economy is not an
exception, suffering with external shocks and internal price volatility through out history,
despite the huge success of Real Plan (1994) in inflation control.
Brazil’s economic instability generated an undesirable outcome in external sector
analysis: in the last years, market expectations have shown a “trade elasticity pessimism”,
with a continuous forecast adjustment, mostly based in a supposed surprising performance of
exports. The adoption of a floating exchange rate regime in 1999 brought a positive
perspective. However, disappointing results of trade balance in 2000 seemed to have
influenced the expectations formation process in following years. Table A shows markets
expectations 6 and 12-months-ahead for exports, imports and the trade balance.
Table A – Market Expectations and Trade Balance
Exports – Forecast
Realized Imports – Forecast
Realized Trade
Balance – Forecast
Realized
12-Months-Ahead Expectations 2000 - 55.09 - 55.84 4.05 -0.75 2001 - 58.22 - 55.57 0.90 2.65 2002 60.50 60.36 55.90 47.24 5.00 13.12 2003 64.50 73.08 49.00 48.29 15.50 24.79 2004 75.90 96.47 56.10 62.74 19.15 33.74
6-Months-Ahead Expectations 2000 - 55.09 - 55.84 2.25 -0.75 2001 - 58.22 - 55.57 -1.32 2.65 2002 56.97 60.36 52.75 47.24 4.40 13.12 2003 66.60 73.08 50.00 48.29 16.90 24.79 2004 85.00 96.47 57.55 62.74 27.82 33.74
NOTE: Inflation expectations are available at the Central Bank of Brazil’s website, on http://www.bcb.gov.br.
These results sustain the structural break’s hypothesis raised in the last years.
Analysts are claiming that the adoption of a floating exchange rate regime has, on the one
hand, stimulated the development of a new composition of exported goods, while, on the
other hand, induced another process of import substitution1. In fact, the transition towards
1 MORAIS and PORTUGAL (2004) found after the first quarter of 2001 a switch towards a “low import growth regime”, in their definition. In a technical note, CAVALCANTI and KAI (2001) found a strong response of the
the new regime implied a nominal exchange rate devaluation of 22.7% in 1999. However,
literature about the theme is still very incipient, since the only known paper dealing with
recent structural breaks in the Brazilian trade balance is MORAIS and PORTUGAL (2004),
where authors estimate a new equation for the demand of import goods.
The traditional approach for time series statistics can be synthesized in three
procedures: a) determinate the degree of stationarity of the evolved data; b) establish a long
run pattern in order to check steady-state conditions; and c) estimate a stationary
representation of the variables’ relationship. The objective of this paper is to estimate new
elasticities for Brazilian imports and exports quantities using time-varying techniques during
the whole procedure, in order to avoid eventual bias in the estimates caused by structural
breaks. Trade elasticities are calculated not only for aggregated data, but also for imports and
exports components. Beyond this introduction, the paper has four sections: in section 2, we
present methodology and a short review of the literature; estimated results and their
properties are presented in section 3; section 4 presents an analysis of economic policy
formulation based in results of the previous sections; section 5 concludes.
Concerning results, it is possible to say, in advance, that structural changes in
Brazilian trade balance are mostly related with a higher degree of exposure of the economy,
instead of an increasing response of imports and exports due to exchange rate variation. The
adoption of new regimes in 1995 and 1999 did not modify trade’s sensitivity for exchange rate
volatility, which, in its turn, has only marginal effects in exports and imports. Another issue is
related with the use of trade balance to obtain a short run equilibrium in the current account,
since exports and imports present very different responses to shocks in the exogenous
variables.
2. Methodology and Review of Literature:
As mentioned in introduction, this section offers details about adopted procedures to
test stationarity, calculate a cointegration vector and combine long and short run relations
among variables in an error-correction model (ECM). It is worth of note that procedures will
be linked by their own results. In this sense, anticipating methodological details, the
endogenous break dates in unit root tests will be the same used to check structural changes in
the cointegration vector; if the structural change hypothesis is confirmed in the test, the new
cointegration vector is adopted in the ECM formulation. This approach seems to be coherent,
since the hypothesis of absence of a structural break does not prejudice the following steps.
exports of basic and semi-manufactured goods after the devaluation. According to authors, manufactured goods did not have the same response because of Argentinean breakdown. RIBEIRO and MARKWALD (2002) emphasize the structural change in the export composition between 1997 and 2001. Similar results are presented in the Inflation Report of December 2003.
Unit roots tests, under the null hypothesis of structural breaks in deterministic
components, have problems in the identification of non-stationary processes2. In this sense,
we adopt as a baseline for testing series in level the procedure suggested in PERRON (1997).
The test selects, under a given criterion, the break point in variable’s trend and constant. Of
course, the procedure has the same properties about loss of power under an over
parameterized test equation3, imposing, then, an equation selection based on the significance
of dummies variables added.
The test allows for the presence of only one break. That can be a major problem when
dealing with irregularities in Brazilian data. One alternative procedure consists in splitting
time series up in two parts, after a prior test to detect the most relevant break in the series.
However, this could bring significant loss of power in a test that already adopts new critical
values in order to deal with the endogenous selection of break point. That is the reason why
this alternative was not adopted.
We applied two processes of endogenous criteria to select break point’s dates: in the
first one, the test minimizes the t-statistic of the autoregressive variable in the test equation;
the second approach maximizes the absolute value of the dummy variables “t” statistics,
looking for a precise definition of the break point. To test the first difference of the series, the
Phillips-Perron (PP) test is adopted as standard, since it has better properties with
heteroskedastic data. Another reason is the impossibility to replicate, with PERRON (1997)’s
procedure, the same break points found in the test with series in levels.
The estimation of a cointegration vector assumes that coefficients may change along
time. To test this hypothesis, a Chow test over the OLS estimative of the cointegration vector
determines the significance of the long-run structural break. The endogenous selection
procedure in the unit root test offers the break points dates to the Chow test. In the case of a
significant break in the long-run coefficient set, the error-correction variable is calculated
based in a combination among those partial sample residuals. This procedure avoids two
potential sources of errors in the estimation process. On the one hand, a mispecified error-
correction term leads to poorer forecasts. On the other hand, in the cases of severe structural
breaks, the stationarity of the cointegration’s residuals may not be verified4. This is, of course,
an a priori rejection of the cointegration hypothesis, based in an ENGLE and GRANGER
(1987) approach.
2 See PERRON (1989) and PERRON (1997). 3 See PERRON (1997) for details about the test. 4 Appendix B presents unit root tests and descriptive statistics for the error-correction term with and without structural breaks.
The basic model structure adopted is an ECM5 with time-varying coefficients. The use
of the ECM formulation seems to be the most indicated among models where variables do
not have the same order of integration. The model has the following structure:
( tttt
n
iitt
m
iitttt xyxLyLcy εθχβα +−+∆+∆+=∆ −−
−−
=− ∑∑ 11
11)()( ) (1)
where yt is the endogenous variable, xt is a vector of exogenous variables and εt is a white
noise residual. In this formulation, all variables are expressed in first differences, except for
the fourth term in the right side, which is the residual of the estimated cointegration
relationship among variables. In this sense, χ represents the variable’s speed of adjustment
towards long run equilibrium.
In this paper, the vector of exogenous variables in the ECM model, ∆xt, has only
lagged variables, in order to avoid additional problems from the absence of weak exogeneity
among variables. The adoption of variables in level or in first difference in the ECM model
and in the cointegration vector will be set by unit root tests. Every ECM model has a constant,
ct, and seasonal factors as deterministic terms. All coefficients, including vectors αt, βt and χt
are time-varying, estimated by the Kalman filter6. We assume that every parameter follows a
random walk as stochastic process. Supposing that υi variables are white noise and
uncorrelated among them, we have:
( )χβα
συ
υχχ
υββυααυ
χ
β
α
,,,,,0~
:2
1
1
1
1
ciiid
with
cc
ii
tt
tt
tt
ctt
=
+=
+=+=+=
−
−
−
−
(2)
The random walk stochastic process for coefficients has two major advantages: on the
one hand, it offers a direct test about the existence of an structural break in the series, since
the variance of the parameters along time is estimated during the process; on the other hand,
this estimation does not restrict the existence of alternative regimes as linear combinations of
alternative states, as in the case of regressions with Markov Switching regimes and a random
walk with a drift.
Another possible time-varying coefficient technique that could have been employed in
the ECM representation is the Markov Switching regression. In these models, the estimated
markovian probability chain expresses the probability of transition among “n” different
5 See HAMILTON (1994), page 580, for details. 6 See HAMILTON (1994), chapter 13, and HARVEY (1991) for details about models in state-space form and the Kalman filter.
regimes. In this kind of model, the selection process for the number of regimes is still an
econometric issue. The most traditional test (Hansen test) has, as the alternative hypothesis,
the suggestion that the data generation process is formed by more than one regime. In this
sense, the Hansen test does not offer a complete answer for the most appropriate number of
regimes. In some sense, this is the major problem found in MORAIS and PORTUGAL
(2004). Regimes’ instability along time, specially for quarterly data, combined with a small
spectrum of possible parameters, given by the number of regimes used, allow us to think that
a random-walk process for parameters could offer better results to model the demand for
imports.
Using the traditional approach in time series econometrics, PAIVA (2003) is the most
recent paper found with a complete set of equations for Brazilian trade. The author estimates
long run elasticities for Brazilian trade and concludes that their values are not very different
from those found in the international literature. For imports, the author found long run
elasticity to the GDP above the unity. However, it must be stressed that this values are
common in Brazilian literature. There is not any evidence of structural breaks in the time
series used, despite shocks during the sample used (period between 1991:01 and 2001:04).
The author also stresses that imports and exports are both influenced by past exchange rate
volatility.
CAVALCANTI and FRISCHTAK (2001), on the other hand, find, at least, one
structural break in imports. There is not an exact definition of the break point, since this
definition relies on assumptions about the nature of the structural change. There are not
evidences about structural breaks in exports. An interesting exercise shows that their
estimates were not contaminated by the “trade elasticity pessimism”, as pointed out in PAIVA
(2003), since the forecasting exercise shows good performance in the exports forecasting.
However, the same performance is not achieved with imports, which were always over-
estimated in out-of-sample exercises. This result may be a consequence of the assumed
hypothesis, especially about domestic GDP7.
3. Estimating Trade Elasticities for Brazil:
3.1. Unit Root Tests and Structural Breaks:
As mentioned in the previous section, the main procedure adopted to test stationarity
on series in levels is based in PERRON (1997), since we allow for the possibility that series
indeed have a structural break. Table B shows tests results8. In general, all tested variables
present traditional characteristics of macroeconomic variables, with a unit root describing
7 See tables 8 and 9, on pages 12 and 13. 8 Traditional ADF and PP tests are available with the author. Description of variables presented in the appendix A of this paper.
the level of the series and stationarity in the first difference. Strictly speaking, only the
capacity utilization of manufacturing industry and the prices of consumer durable goods
must be seen as stationary. Since these variables rejected the unit root hypothesis for series
in levels, they are treated as stationary.
There are some common features that can be inferred from estimated break points. In
imports, the first half of the 90’s seem to concentrate the most important breaks in series: the
reduction of tariffs in the late 80’s (due to the Mercosur commercial agreement), followed by
trade liberalization during President Collor’s government, generated a new turning point in
imports. It is also worth of note a coincidence in estimated imports break dates, since those
related with prices have always preceded structural changes in quantities. Imports quantities
have stabilized in a higher level after the Real Plan (July, 1994), while prices keep falling after
price stabilization. In this sense, the 90’s trade liberalization can be seen as a permanent
shock in relative prices for Brazil, since these changes resulted in a new imports level. The
same pattern is not repeated in exports, which estimated breaking points in prices do not
have coincidence even among its components. Graph 1 shows the distribution of breaks in the
shaded area and the time series of exports and imports prices and volumes.
GRAPH 1 – Estimated Breaks, Exports and Imports – 1977-2004 Imports - Prices
4.00 4.20 4.40 4.60 4.80 5.00 5.20
1978
01
1979
01
1980
01
1981
01
1982
01
1983
01
1984
01
1985
01
1986
01
1987
01
1988
01
1989
01
1990
01
1991
01
1992
01
1993
01
1994
01
1995
01
1996
01
1997
01
1998
01
1999
01
2000
01
2001
01
2002
01
2003
01
2004
01
Period
LOG
(Índe
x)
M - bens de capital - preços M - consumo duráveis - preços M - consumo não duráveis - preçosM - intermediários - preços M - preços
Export - Prices
4.0
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
1977
01
1978
01
1979
01
1980
01
1981
01
1982
01
1983
01
1984
01
1985
01
1986
01
1987
01
1988
01
1989
01
1990
01
1991
01
1992
01
1993
01
1994
01
1995
01
1996
01
1997
01
1998
01
1999
01
2000
01
2001
01
2002
01
2003
01
2004
01
Period
LOG
(Inde
x)
X - básicos - preços X - manufaturados - preços X - semi-manufaturados - preços X - preços
Imports - Quantum
2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50
1978
01
1979
01
1980
01
1981
01
1982
01
1983
01
1984
01
1985
01
1986
01
1987
01
1988
01
1989
01
1990
01
1991
01
1992
01
1993
01
1994
01
1995
01
1996
01
1997
01
1998
01
1999
01
2000
01
2001
01
2002
01
2003
01
2004
01
Períod
LOG
(Inde
x)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
LOG
(Index) - Consum
er Durable
M - bens de capital - quantum M - intermediários - quantum M - quantumM - consumo duráveis - quantum M - consumo não duráveis - quantum
Exports - Quantum
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
1977
01
1978
01
1979
01
1980
01
1981
01
1982
01
1983
01
1984
01
1985
01
1986
01
1987
01
1988
01
1989
01
1990
01
1991
01
1992
01
1993
01
1994
01
1995
01
1996
01
1997
01
1998
01
1999
01
2000
01
2001
01
2002
01
2003
01
2004
01
Period
LOG
(Inde
x)
X - básicos - quantum X - manufaturados - quantum X - semi-manufaturados - quantum X - quantum
About unit root tests applied in other variables, some common results in the
literature9 are presented in series that measure Brazilian economic activity. The growth
slowdown after the introduction of the so-called “Collor Plan” is pointed as a major structural
break in real GDP series and in capacity utilization of manufacturing industry. The period
between the end of the 80’s and the beginning of the 90’s is also identified as a break point
for the real effective exchange rate.
3.2. Trade Elasticities: Brazilian Exports
The general model for exports volume includes three exogenous variables: the real
effective exchange rate, the specific export price and the real world imports, as a proxy for
world’s demand. Estimated cointegration vectors for the whole and for partial samples are
presented in table C. Chow test’s results show that the structural break hypothesis for exports
has major influence for long run inference. All significant coefficients have the expected sign,
but their magnitude present important changes among different sample sizes. Comparing
with literature, results are pretty much in line with CAVALCANTI and FRISCHTAK (2001)
and, after break points, with PAIVA (2003). There are some major changes in the
significance of some parameters, mostly in those related with the real exchange rate. The
proxy for the world’s demand has large significance in equations estimated after break dates.
9 See MORAIS and PORTUGAL (2004) for similar results. There is only one major difference in the test for the capacity utilization of manufacturing industry. The authors estimated the test under the hypothesis of a broken trend. We have adopted the level shift hypothesis for the test, since the significance of the trend dummy presents sharp variations as the break point selection procedure changes.
Table B – Unit Root Tests
Variable Test 1 – Maximize Break Probability Break Point Date
Test 2 – Minimize Unit Root
Probability Break Point Date
PP – First Difference
Total Exports — Volume — LQX -3.43184 (t) 1995:03 -3.75071 2002:01 -15,60263*
emi-manufactured goods — Exports — Volume — LQXS -4.99853 (t) 1991:02 -5.18386 (t) 1993:04 -13,38384*
Manufactured goods — Exports — Volume — LQXMAN -3.99515 (t)
1984:03 -3.99515 (t) 1984:03 -12,85234*
Basic goods — Exports — Volume — LQXB -4.28519 (t) 1997:01 -4.38325 (t) 1998:04 -23,03229*
Total Imports — Volume — LQM -2.80707 (t) 1994:02 -4.21685 1992:02 -11,35088*
Non-Durable Goods — Imports — Volume — LQMND -4.38724 (t) 1994:02 -4.38724 (t) 1994:02 -11,47738*
Consumer Durable Goods — Imports — Volume — LQMD -2.38321 (t) 1993:04 -2.74771 (t) 1993:01 -10,06614*
Capital Goods — Imports — Volume — LQCAP -2.70613 (t) 1994:02 -3.98617 1992:02 -15,60582*
Intermediate Goods — Imports — Volume — LQINT -2.13367 1983:02 -4.12905 1992:03 -10,93235*
Total Exports — Prices — LPX -4.33778 (t) 1994:01 -4.34870 (t) 1992:04 -9,367779*
Semi-manufactured goods — Exports — Prices — LPXS -4.41029 (t) 1993:03 -4.82910 1987:02 -6,666389*
Manufactured goods — Exports — Prices — LPXMAN -4.79160 (t) 1992:04 -4.79160 (t) 1992:04 -7,467081*
Basic goods — Exports — Prices — LPXB 0.58286 (t) 1997:04 -4.07908 (t) 1995:02 -10,39538*
Total Imports — Prices — LPM -1.65609 (t) 1986:02 -5.02016* 1986:03 -10,33210*
Non-Durable Goods — Imports — Prices — LPMND -4.24210 (t) 1992:01 -5.19883* 1988:02 -10,98044*
Consumer Durable Goods — Imports — Prices — LPMD -6.33470* (t) 1990:02 -6.33470* (t) 1990:02 -11,96310*
Capital Goods — Imports — Prices — LPCAP -4.65085 (t) 1989:02 -4.65085 (t) 1989:02 -15,19493* (t)
Intermediate Goods — Imports — Prices — LPINT -3.49947 (t) 1989:03 -4.40978 (t) 1986:04 -8,231295*
Real Effective Exchange Rate — REER -1.83113 (t) 1991:03 -3.43382 1988:04 -8,159108*
World’s Real Imports – LWM -4.43191 (t) 1986:04 -5.31357 (t) 2002:02 -23,65730*
Capacity Utilization in Manufacturing Industry – LNCUa -5.06131* 1989:04 -5.06737* 1990:04 -14.72967*
Gross Domestic Product – Brazil – LGDP -4.73712 (t) 1990:01 -4.97102 (t) 1989:03 -14,49408*
Note: all variables expressed in natural logarithms. (*) indicates stationarity at 5%. (t) indicates the use of a deterministic trend dummy in the test equation for series in levels. Critical values of unit root tests with structural breaks in PERRON (1997). (a) The unit root test was applied in the series transformed in a logit function, since this is a truncated variable. For details, see CORSEUIL, GONZAGA and ISSLER (1996)
The significance of the Chow test leads us to use a composed cointegration vector in
the ECM model, based on the residuals of the sub-samples estimations. The structural breaks
found in cointegration relations have some major implications in forecasting exercises. For
instance, the increases in the long run elasticities of world’s income and export prices can be
seen as a potential source of forecast error. For instance, the surprising growth in world’s
imports verified in 2003 (6.79% in 2003, compared with 2002), using the cointegration
vector generated by the full sample, would result in a long run growth of total exports
quantity of 7.13%. On the other hand, using the vector generated by the partial sample, the
same shock results in an estimated increase of 10.63% in total exports.
TABLE C – Cointegration Vectors – Exports’ Volume LQX – Break date: 1995:03 LQXS – Break date: 1991:02
Full Sample Before
Break Date After Break
Date Full
Sample Before
Break Date After Break
Date
REER 0.258366* (0.115484)
0.898509* (0.161045)
0.239757 (0.190252)
-0.195470 (0.152984)
0.949551* (0.165383)
0.264650 (0.245661)
LWM 1.049294*
(0.040443) 1.707892* (0.120993)
1.565016* (0.120446)
1.429734* (0.06081)
3.173774* (0.170979)
0.838000* (0.101980)
Export Prices -0.032976 (0.256221)
-0.149051 (0.307814)
0.695466* (0.344275)
0.053296
(0.222864) -0.316411
(0.210659) 0.192340
(0.245683)
Constant -2.795177
(1.580670) -8.868366* (1.996591)
-9.326024* (2.630384)
-3.666204* (1.694232)
-16.91172* (1.788567)
-2.607986 (1.773665)
Chow Test – P-Value 0.000000 0.000000
LQXMAN – Break date: 1984:03 LQXB – Break date: 1997:01
Full Sample Before
Break Date After Break
Date Full
Sample Before
Break Date After Break
Date
REER 0.598759* (0.138900)
1.561368* (0.321477)
-0.086133 (0.109046)
0.437535* (0.100113)
0.271802 (0.137979)
0.502824 (0.403284)
LWM 0.965336* (0.056972)
0.533475 (0.816926)
0.862360* (0.039928)
0.803981* (0.05008)
0.644660* (0.097614)
2.285375* (0.307176)
Export Prices 1.204338* (0.266862)
1.855250* (0.382800)
-1.018515* (0.249079)
-0.530035* (0.148979)
-0.669664* (0.163137)
0.297567 (0.381415)
Constant -9.306451* (1.530158)
-14.40245* (3.606323)
4.361077* (1.446764)
0.151595 (1.158501)
2.454593 (1.413700)
-13.37620* (3.990279)
Chow Test – P-Value 0.000000 0.000079
Note: (*) indicates significance at 5%. Standard errors are in parenthesis.
Analyzing the ECM representation, one important issue is the significance of
estimated variance coefficients (the σ2i parameters of equation (2)). This is another structural
break test, since an ECM with estimated coefficient variance indifferent from zero is
equivalent of a traditional ECM representation. Table D presents the structure of the selected
models of exports quantities and Wald tests about coefficients’ variance significance. There
are significant signs of change in the trend of the series, frequently related to manufactured
and basic goods, based in the evaluation of variance of constant and seasonal dummies
variables.10 However, estimations for aggregated exports do not point out to the existence of
significant structural changes in parameters. The most relevant changes in short-run
elasticities are presented in the price and world’s income elasticity of manufactured goods.
Surprisingly, despite significant economic policy changes in the period, parameters related
with exchange rate variations are quite stable, since they only had some significance being
fixed along time.
TABLE D – ECM Model Selection and Coefficients’ Variance – Volume of Exports LQX LQXS LQXMAN LQXB
Model Selection – Number of Lags of Each Exogenous Variable Real Effective Exchange Rate 0 1 1 2
World’s Real Imports 1 1 1 1 Export Prices 2 1 2 1
Autoregressive 2 2 2 1
Cointegration 1 1 1 1
Coefficients Variance – Significance Test ( σi2 = 0)
Joint Significance 1.751944 (0.6254)
4.665914 (0.3233)
11.44648 (0.0220)
7.522197 (0.1846)
Constant + Seasonal 6.58E-19
(1.000000) 1.254043
(0.534181) 178.8423
(0.000000) 313.0521
(0.000000) Real Effective Exchange Rate – – – –
World’s Real Imports 1.184458
(0.276450) 1.382797
(0.239625) 3.597984
(0.057850) 1.096169
(0.295108)
Export Prices – – 6.417822
(0.011298) 0.251951
(0.615704)
Autoregressive – 1.771853
(0.183153) – –
Cointegration 0.160243
(0.688933) – – 0.261645
(0.608992) Note: values in parenthesis express p-values. All equations include constant and seasonal dummies. Values not reported imply absence of the variable in the equation or a fixed coefficient along time.
In order to evaluate parameter’s variation along time, graph 2 plots the evolution of
the smoothed coefficients of export prices and world’s income elasticity, while table E
presents results for the last period of sample. It is worth of note the absence of a pattern in
dates of structural breaks. While the world’s income elasticity of manufactured goods has
stabilized after 1994, those associated for total, basic and semi-manufactured goods exports
have grown in the period. In this sense, the so-called “tendency of Brazilian trade towards
foreign markets” can not be seen as a recent phenomenon, since the increase of exports’
sensitivity to the world’s income has been constant since 1994. Considering the importance of
Brazil in price structure of a wide variety of commodities, the increase in price elasticity of
these goods since 1986 can also be seen as long-term trend for exports. This pattern is not
10 The seasonal dummy variables here are not orthogonalized. In this sense, they do affect the mean and the trend of the series.
repeated in the price elasticity of manufactured goods, which presents a striking change in
1987.
GRAPH 2 – Short Run Time-Varying Elasticities – Exports – 1977-2004Q2 World's Real Income Elasticity
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1977
Q1
1978
Q1
1979
Q1
1980
Q1
1981
Q1
1982
Q1
1983
Q1
1984
Q1
1985
Q1
1986
Q1
1987
Q1
1988
Q1
1989
Q1
1990
Q1
1991
Q1
1992
Q1
1993
Q1
1994
Q1
1995
Q1
1996
Q1
1997
Q1
1998
Q1
1999
Q1
2000
Q1
2001
Q1
2002
Q1
2003
Q1
2004
Q1
Period
Total Exports Basic Goods - Exports Semi-manufatured Goods - Exports Manufactured Goods - Exports
Export Prices' Elasticities
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1977
Q1
1978
Q1
1979
Q1
1980
Q1
1981
Q1
1982
Q1
1983
Q1
1984
Q1
1985
Q1
1986
Q1
1987
Q1
1988
Q1
1989
Q1
1990
Q1
1991
Q1
1992
Q1
1993
Q1
1994
Q1
1995
Q1
1996
Q1
1997
Q1
1998
Q1
1999
Q1
2000
Q1
2001
Q1
2002
Q1
2003
Q1
2004
Q1
Period
Basic Goods - Exports Manufactured Goods - Exports
Short run exports’ elasticities presented in table E offer some interesting details about
trade’s dynamics. The only short-run elasticity that is significant is the world’s income
elasticity of manufactured goods. Transportation vehicles (planes and cars) and iron and
steel products, naturally correlated with the business cycle, mainly constitute this group of
products in Brazilian exports. The absence of short run significance does not invalidate
estimated cointegration vectors for exports presented in table C, since long-run adjustment
coefficients in the ECM are significant at levels lower than 10%.
In order to test estimation’s robustness, we tested two departures from the
benchmark model: in the first one, one lag of the real exchange rate volatility, as defined in
PAIVA (2003), is included as explanatory variable; the second model tests, together with real
exchange volatility, the influence of internal economic activity over exports, including one lag
of the capacity utilization of manufacturing industry11. This variable is used in CAVALCANTI
and FRISCHTAK (2001), supposing that, for some industries, foreign trade is seen as a
secondary market, complementing sales when demand in Brazil is not high. Of course, the
hypothesis implies a strictly negative sign for the variable. Table F presents the variability of
those estimated parameters and its significance. Both variables were not included in the
cointegration vector, since tests do not reject the stationarity hypothesis.
Presented results show that, in general, the benchmark model is, indeed, a good
specification for exports, since the inclusion of these variables does not have a major
influence in results. In all cases, changes in the estimated parameters, compared with the
benchmark formulation, were insignificant. Consequently, inference based in the final state
vector can be done without further problems. It is also impossible to make any inference
11 Technically speaking, the last formulation, including both variables, is said to be nesting our benchmark model, in the sense that the last one is a special case of the former, imposing the hypothesis that both coefficients and their variance are equal to zero. See GREENE (2000), chapter 7 for hypothesis testing of nested and non-nested models.
about the relation between economic activity and exports, since all estimates do not present
any significance.
TABLE E – Trade Elasticities – Volume of Exports – 2004Q2 LQX LQXS LQXMAN LQXB
Constant -0.027889* (0.008705)
-0.018078 (0.017728)
0.056952 (0.039674)
-0.220874* (0.015305)
Seasonal 1 -0.062775 (0.074185)
-0.016493 (0.113328)
-0.235349* (0.083543)
0.194202* (0.074369)
Seasonal 2 0.193553* (0.042614)
0.023740 (0.070104)
0.146471* (0.065181)
0.582181* (0.073935)
Seasonal 3 0.077968
(0.062363) 0.108704
(0.078889) -0.064740 (0.068364)
0.284030* (0.090167)
D(REERt-1) – 0.370674
(0.227142) 0.150144
(0.193879) –
D(REERt-2) – – – -0.034954 (0.034582)
D(LWMt-1) 0.394236
(0.472166) 1.016192
(0.948585) 1.669190* (0.744736)
-0.164912 (0.913047)
D(LWMt-2) – – – –
D(Export Pricest-1) – 0.389632
(0.335794) -0.566898 (0.592494)
0.692545 (0.485471)
D(Export Pricest-2) 0.102372
(0.073879) – 0.010871
(1.943271) –
AR(1) -0.020870 (0.080682)
-0.085596 (0.103523)
-0.083103 (0.148972)
0.102745 (0.128322)
AR(2) -0.173194
(0.092661) -0.382565* (0.173669)
-0.213222 (0.157958) –
Cointegration -0.292244* (0.138927)
-0.289715* (0.146348)
-0.181492 (0.106148)
-0.656454* (0.170379)
Note: (*) indicates coefficient significance at 5%. Standard errors are in parenthesis.
One interesting feature of data is the low influence of exchange rate regime in the
structure of exports. All estimated smoothed elasticities have very low significance and do not
show signs of structural changes due to regime changes. Actually, PAIVA (2003) tested the
inclusion of the volatility of the real exchange rate as an explanatory variable for exports,
obtaining significant results. Perhaps, the author’s sample did not offer enough observations
to make an appropriate consideration about structural breaks. In this sense, a model
including a variable with significant ruptures could incorporate, in fact, structural changes
captured in our models. Some evidence of it can be observed comparing the estimated
coefficients of long run adjustment in the ECM model, which are closer with those in PAIVA
(2003) only when the author uses the real exchange rate volatility.
TABLE F – Alternative Models – Volume of Exports LQX LQXS LQXMAN LQXB
Model with Real Exchange Rate Volatility
Variance of parameter in time – Significance test – Var(REER)
– – – –
Final State – 2004 Q2 0.007878
(0.192403) 0.009387 (0.194101)
0.005897 (0.322183)
0.004482 (0.558897)
Model with Real Exchange Rate Volatility and Capacity Utilization in Manufacturing Industry
Variance of parameter along time – Significance test – Var(REER)
– – 2.827E-07 (0.941256)
–
Final State – 2004 Q2 0.007623
(0.203447) 0.009310
(0.203828) 0.006283
(0.193460) 0.004700
(0.516083) Variance of parameter along time – Significance test – LNCU – –
2.755E-06 (0.872668) –
Final State – 2004 Q2 -0.166653
(0.450633) 0.173854
(0.479604) -0.060509 (0.747983)
-0.275527 (0.458153)
Note: values in parenthesis express p-values. Complete estimations are available with the author. 3.3. Trade Elasticities: Brazilian Imports
The ECM formulation for imports has currently three variables in the cointegration
vector and four exogenous components. Real effective exchange rate, Brazilian GDP and
import prices form the cointegration vector12. Their first difference and the level of
manufacturing industry capacity utilization also compose the ECM as exogenous variables.
Results about the long run relationship among variables for imports are presented in table G.
Estimated long-run vectors does not have problems with signs that conflict with traditional
economic theory. However, major variations appear when comparing the absolute value of
coefficients in partial sample estimations. As an example, magnitudes of income elasticity
coefficients are mostly above the unity, with a maximum of 6.69 for durable goods in the full
sample and a minimum of –0.50 for capital goods after the estimated structural break date.
Estimated income elasticity of imports above the unity are common in literature: despite an
estimated value of 0.821, MORAIS and PORTUGAL (2004) quote five studies with that
result; PAIVA (2003) also finds similar values for imports components, with a minimum
value of 2.1 for non-durable goods; CAVALCANTI and FRISCHTAK (2001) find a minimum
value of 1.91 for capital goods and 3.39 for total imports.
Table H reports the estimated parameter variance for imports models. Evidences of
structural change in aggregate imports are shown in trend and error correction term’s
variance. Coefficients related with utilization of manufacturing capacity are very stable.
However, there are signs of changes in the income elasticity of capital and intermediate
goods. These components, and also non-durable goods imports, seem to present some
changes in price elasticity. Results about cointegration among variables have to be seen with
some caution, since there is evidence of instability in the coefficient of long-run adjustment.
12 Prices do not appear in the consumer durable goods’ cointegration equation, since it was identified as stationary.
TABLE G – Cointegration Vectors – Imports’ Volume LQM – Break date: 1994:02 LQMND – Break date: 1994:02
Full
Sample Before
Break Date After Break
Date Full
Sample Before
Break Date After Break
Date
REER -0.305636 (0.190803)
-1.081744* (0.183212)
-0.590565* (0.196894)
-0.839609* (0.331790)
-0.736575 (0.443107)
-0.427481 (0.224664)
LGDP 2.674177*
(0.446066) -1.242275* (0.473126)
1.775718* (0.574581)
5.514393*
(0.400845) 3.711906* (0.723413)
3.051712* (0.916861)
Import Prices
-0.936529 (0.542726)
-1.426271* (0.376176)
-1.235345* (0.572762)
0.331332
(0.490897) 0.587505
(0.585876) 1.327725* (0.456613)
Constant -2.738554 (4.996565)
20.83075* (4.355142)
4.358349 (4.497682)
-19.83824* (5.162051)
-13.35035 (6.726967)
-14.23273* (6.219776)
Chow Test – P-Value 0.000000 0.000000
LQMD – Break date: 1993:04 LQCAP – Break date: 1994:02
Full
Sample Before
Break Date After Break
Date Full
Sample Before
Break Date After Break
Date
REER -2.182073* (0.386781)
-2.040281* (0.482005)
-2.304667* (0.544268)
-1.743417* (0.198640)
-1.866952* (0.241649)
-0.250507 (0.364041)
LGDP 6.692385* (0.461478)
2.408434* (0.993825)
1.421932 (1.393993)
3.306110* (0.208980)
1.530024* (0.660350)
-0.499926 (1.902083)
Import Prices - - -
-2.640002* (0.204626)
-2.015077* (0.341292)
-1.448449 (1.254117)
Constant -18.27304* (3.274822)
0.365258 (6.150342)
7.957097 (5.255289)
8.432937* (2.224262)
14.11474* (2.923702)
14.81441 (13.87010)
Chow Test – P-Value
0.000000 0.001137
LQINT – Break date: 1983:02
Full Sample Before Break Date After Break Date
REER -1.307582* (0.133286)
-0.339899 (0.466290)
-1.123941* (0.136064)
LGDP 2.427818* (0.151537)
4.362610 (2.208399)
3.050088* (0.200408)
Import Prices
-3.084017* (0.231079)
-3.459579 (2.750157)
-2.478411* (0.257390)
Constant 12.74335* (2.134421)
1.517075 (21.04925)
6.204796* (2.542920)
Chow Test – P-Value 0.000491
Note: Prices of consumer durable goods were not included in the cointegration vector because unit root test support the stationarity hypothesis. (*) indicates significance at 5%.
The short-run elasticities presented in graph 3 show some interesting features of
Brazilian imports. It is worth of note the price effects of structural changes in imports: the
signs of these coefficients are becoming more negative along time. The positive sign, verified
before 1990, can be ascribed to the degree of openness of Brazilian economy, since the
Brazilian economy has a mainly focus in intermediate and capital goods imports. In this
sense, the volume of imports seemed to be inelastic to price variations at that time.
TABLE H – ECM Model Selection and Coefficients’ Variance – Volume of Imports LQM LQMND LQMD LQCAP LQINT
Model Selection – Number of Lags of Each Exogenous Variable Real Effective Exchange Rate
2 0 2 2 0
GDP 1 0 1 1 2 Capacity Utilization 2 1 1 2 2
Import Prices 0 1 0 2 2
Autoregressive 1 1 2 2 1
Cointegration 1 1 1 1 1
Coefficients Variance – Significance Test ( σi2 = 0)
Joint Significance 17.00942
(0.074156) 304.2739
(0.000000) 1.662439
(0.998334) 14.87841
(0.248153) 38.56611
(6.2759E-05)
Constant + Seasonal 6.863759
(0.143265) 67.87321
(6.3838E-14) 1.252376
(0.869402) 0.309585
(0.989187) 4.048449
(0.399489) Real Effective Exchange Rate
0.640837 (0.423408) –
2.00E-05 (0.996432)
1.258673 (0.261902) –
GDP 0.003892
(0.950256) –
1.76E-10 (0.999989)
2.119972 (0.145389)
2.392198 (0.302371)
Capacity Utilization 4.32E-07
(0.999475) 0.549996
(0.458319) 1.74E-15
(1.000000) 6.95E-09
(0.999933) 0.000438 (0.999781)
Import Prices – 34.86212
(3.5390E-09) – 3.002450
(0.222857) 1.449910
(0.228542)
Autoregressive 0.040320
(0.840856) 3.894326
(0.048449) 4.10E-06
(0.999998) 0.163295
(0.921597) 0.001477
(0.969341)
Cointegration 0.955939
(0.328212) 11.69973
(0.000625) 8.00E-16
(1.000000) 4.13E-13
(1.000000) 0.477820
(0.489411) Note: values in parenthesis express p-values. All equations include constant and seasonal dummies.
The income elasticity has some particular details that must be stressed. First of all,
there is a downward trend in the income elasticity of intermediate goods. Despite its high
level even nowadays, this trend may result, in the long run, in an equivalent movement on
aggregate income elasticity, since these goods represents around 60% of total imports.
Capital goods income elasticity shows two periods of high values, located between 1984 and
1988, and 1994 and 1999. These periods of high-income elasticity coincides with periods of
continuous growth in the economy, despite de large volatility in prices, during the first
period, and the large volatility in economic growth13. In this sense, the hypothesis of a
continuous growth for a long period may imply in problems for the trade balance, since there
would be a strong pressure from a component with large participation in total imports.
Conversely, this bad equilibrium could be avoided by a higher productivity of the economy.
13 The average GDP growth per year between 1984 and 1988 was of 4.84%, and between 1994 and 1999 was of 3.23%. On the other hand, the period between 1980 and 1983, marked by the external debt crises, had an average growth of –2.12%; 1991-1993 period had an average growth of 0.84%; after 1999, the average growth of GDP per year was of 1.63%, until 2003.
GRAPH 3 – Short Run Time-Varying Elasticities – Imports – 1980-2004Q2 Imports' Income Elasticity
-1
0
1
2
3
4
5
6
19
80Q
1
19
81Q
1
19
82Q
1
19
83Q
1
19
84Q
1
19
85Q
1
19
86Q
1
19
87Q
1
19
88Q
1
19
89Q
1
19
90Q
1
19
91Q
1
19
92Q
1
19
93Q
1
19
94Q
1
19
95Q
1
19
96Q
1
19
97Q
1
19
98Q
1
19
99Q
1
20
00Q
1
20
01Q
1
20
02Q
1
20
03Q
1
Period
Total Imports Durable Goods Intermediate Goods Capital Goods
Real Effective Exchange Rate Elasticity
-1.00
-0.50
0.00
0.50
1.00
1.50
19
80Q
1
19
81Q
1
19
82Q
1
19
83Q
1
19
84Q
1
19
85Q
1
19
86Q
1
19
87Q
1
19
88Q
1
19
89Q
1
19
90Q
1
19
91Q
1
19
92Q
1
19
93Q
1
19
94Q
1
19
95Q
1
19
96Q
1
19
97Q
1
19
98Q
1
19
99Q
1
20
00Q
1
20
01Q
1
20
02Q
1
20
03Q
1
Period
Total Imports Durable Goods Capital Goods
Capacity Utilization Elasticity
-1.50
-1.00
-0.50
0.00
0.50
1.00
19
80Q
1
19
81Q
1
19
82Q
1
19
83Q
1
19
84Q
1
19
85Q
1
19
86Q
1
19
87Q
1
19
88Q
1
19
89Q
1
19
90Q
1
19
91Q
1
19
92Q
1
19
93Q
1
19
94Q
1
19
95Q
1
19
96Q
1
19
97Q
1
19
98Q
1
19
99Q
1
20
00Q
1
20
01Q
1
20
02Q
1
20
03Q
1
Period
Total Imports Durable Goods Non-Durable Goods Intermediate Goods Capital Goods
Import Prices' Elasticities
-6.00
-4.00
-2.00
0.00
2.00
4.00
6.00
8.00
19
80Q
1
19
81Q
1
19
82Q
1
19
83Q
1
19
84Q
1
19
85Q
1
19
86Q
1
19
87Q
1
19
88Q
1
19
89Q
1
19
90Q
1
19
91Q
1
19
92Q
1
19
93Q
1
19
94Q
1
19
95Q
1
19
96Q
1
19
97Q
1
19
98Q
1
19
99Q
1
20
00Q
1
20
01Q
1
20
02Q
1
20
03Q
1
Period
Non-Durable Goods Intermediate Goods Capital Goods
One surprising result of estimation is the negative sign found of the coefficient of the
capacity utilization level. It implies that a high use of industry’s capacity leads towards the
reduction in imports’ growth. MORAIS and PORTUGAL (2004) also find the same result
using quarterly and annual data. One possible explanation for this result is the substitution of
imports for internal production in the upward period of the business cycle. In this sense, the
recovery of economic activity starts with an increase in imports, when growth is not yet wide-
spread, followed by a period where internal production substitutes foreign trade14. Graph 4
plots the cross correlogram among the growth rate of imports and the industry capacity level.
Table I presents estimated short run coefficients for imports. In opposite to export
results, there are many significant estimated coefficients in the ECM representation. Despite
their heavy influence in the import’s index composition, the intermediate goods and the non-
durable goods model rejected the cointegration hypothesis. In the case of non-durable goods,
however, there are two periods where the ECM representation is valid: from 1982 to 1987 and
from 1993 to 2001, the long-run adjustment coefficient is significant at 5%. This type of
result, on the other hand, has never happened in the case of intermediate goods. Graph 5
presents the estimated coefficient path and standard errors for these two variables.
14 See the next section for details about the relationship of imports and the business cycle.
GRAPH 4 – Cross Correlation: Quarterly Variation of Imports (t) and Industry’s Capacity Utilization (t+i)
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
-4 -3 -2 -1 0 1 2 3 4
GRAPH 5 – Long-Run Adjustment Coefficient – Intermediate and Non-Durable Goods
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0
1980Q1
1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
Non-Durable Goods -Cointegration
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
19
83
Q1
19
83
Q4
19
84
Q3
19
85
Q2
19
86
Q1
19
86
Q4
19
87
Q3
19
88
Q2
19
89
Q1
19
89
Q4
19
90
Q3
19
91
Q2
19
92
Q1
19
92
Q4
19
93
Q3
19
94
Q2
19
95
Q1
19
95
Q4
19
96
Q3
19
97
Q2
19
98
Q1
19
98
Q4
19
99
Q3
20
00
Q2
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
Intermediate Goods - Cointegration
It is also worth of note that results negatively relating industry’s capacity utilization
and imports components are consistent with aggregated data. That is an important result,
since it proves that the negative relation established between imports and one lag of capacity
utilization is not spurious. Only the coefficient associated with capital goods has positive sign.
The stability of these coefficients is also an important factor relating disaggregated data with
results from total imports ECM model.
Following again the same procedure adopted for exports, table J presents results for
alternative models, including the real effective exchange rate volatility in imports equation. A
similar pattern found in exports was repeated here, with very stable coefficients along time
and real exchange rate volatility slightly offering some information in the durable goods
model, at 10%. In this sense, the conclusions found in PAIVA (2003) do not seem to be
robust under another set of hypothesis about the structure of the model.
TABLE I – Trade Elasticities – Volume of Imports – 2004Q2 LQM LQMND LQMD LQCAP LQINT
Constant 0.449397
(0.908924) 5.695317*
(2.053697) 4.797578* (1.911159)
-3.314341 (1.785282)
2.191927* (0.897729)
Seasonal 1 0.012250
(0.037897) -0.278892* (0.085800)
-0.176974 (0.096112)
-0.216240* (0.060603)
0.165878* (0.042076)
Seasonal 2 0.112714*
(0.055029) -0.173004 (0.146699)
0.146728 (0.140270)
-0.110302 (0.069191)
0.223443* (0.076081)
Seasonal 3 -0.011230
(0.058935) -0.063953 (0.282453)
-0.129771 (0.094637)
-0.087619 (0.064339)
0.120672 (0.069478)
D(REERt-1) – – – – –
D(REERt-2) -0.172591
(0.246188) – 0.171795
(0.374906) -0.827297 (0.557427) –
D(LGDPt-1) 2.810778* (0.414777) –
2.167473* (0.897930)
0.080267 (2.184410)
3.821081* (0.483436)
D(LGDPt-2) – – – – -0.003696 (1.056271)
D(LNCUt-1) -0.815820* (0.256742)
-1.273014* (0.468512)
-1.091686* (0.437731) –
-1.018826* (0.274996)
D(LNCUt-2) 0.701912*
(0.244498) – – 0.757057
(0.405852) 0.487168
(0.261859)
D(Import Pricest-1) – -0.738628 (2.081173)
– -1.190702 (1.036123)
–
D(Import Pricest-2) – – – -0.405430 (0.370060)
0.342925 (1.019314)
AR(1) -0.135187
(0.092942) -0.024346 (0.110248)
0.256865* (0.096526)
-0.469675* (0.100288)
-0.341857* (0.098499)
AR(2) – – 0.013550
(0.105971) -0.143088 (0.157906)
–
Cointegration -0.328257* (0.163970)
-0.619108 (0.478236)
-0.165049* (0.055474)
-0.261129* (0.089046)
-0.095996 (0.123079)
Note: (*) indicates coefficient significance at 5%. Standard errors are in parenthesis.
TABLE J –Models with Exchange Rate Volatility – Volume of Imports
LQM LQMND LQMD LQCAP LQINT Variance of parameter along time – Significance test – Var(REER)
3.17E-07 (0.999551)
3.98E-15 (1.000000)
4.33E-09 (0.999948)
1.48E-11 (0.999997)
3.42E-06 (0.998525)
Final State – 2004 Q2 0.002164
(0.550310) 0.007331
(0.361193) 0.017454
(0.074593) 0.000983
(0.894446) -0.003525 (0.366986)
Note: values in parenthesis express p-values. Complete estimations are available with the author.
4. Economic Policy and Trade:
The discussion above raises three relevant topics influencing the view of foreign trade
as an economic policy instrument. First, the structural break hypothesis and its relative
importance in recent years must be evaluated in forecasting exercises. Second, the negative
and significant sign of capacity utilization in imports models raises issues about the pattern
of the Brazilian business cycle. Third, of course, it is necessary to stress the role of trade
balance in Brazil as an instrument to achieve external sector equilibrium in the short run.
In order to evaluate the role of structural breaks in forecasting exercises, table K
presents a small in-sample exercise starting in 199915, based in total imports and exports
models, using two scenarios. The main objective is decomposing the forecast error in two
parts: an ordinary forecasting error, due to information not captured by the model, and the
error caused by misspecification of model’s parameters. Indeed, as shown in table, the use of
time-varying coefficients did not generate gains in forecasting. Evaluation of the average
absolute errors shows that the use of time-varying techniques could only reduce, at 5% of
significance, deviations for imports in the one-step-ahead horizon. Thus, despite the
historical higher volatility in imports time-series, the use of time-varying coefficients does
not imply a higher increase in model’s forecasting capacity.
TABLE K – Forecasting Exercises – 1999Q1 to 2003Q4 – Imports and Exports – Mean Absolute Errors – Variables in LN
Exports Imports Number of
Quarters Ahead 1999Q1 Parameters
Time-Varying Parameters
1999Q1 Parameters
Time-Varying Parameters
One Quarter 0.047880 0.047668 0.062926 0.052432
Two Quarters 0.109100 0.105605 0.138897 0.142122
Three Quarters 0.184279 0.176627 0.262824 0.264709
Four Quarters 0.264725 0.248846 0.420056 0.420618
Earlier results do support the structural break hypothesis in Brazilian foreign trade,
but only before 1999. Most of the “surprise” in trade results must be assigned to external
positive shocks, in the exports side, and the disappointing results of Brazilian economy in
2002 and 2003. Indeed, the growth of the world in 2002-2003 produced some impressive
results: world’s real imports, excluding Brazilian imports, compared with the same period of
the previous year, rose from 4.23% in 2002 to 9.40% in the twelve months finished in the
second quarter of 2004; Brazilian export prices, which had fallen 4.57% in 2002, rose 4.69%
in 2003. It is evident that an increasing world’s income elasticity in Brazilian imports would
magnify these results. However, the “structural change factor” after 1999 plays a minor role
in explaining exports quantities.
Concerning imports, results must be seen with some care, since they seem to be
influenced by a new pattern of growth in the last years. Table L lists the last three periods of
economic expansion, characterized by the presence of a clear and low-variance positive trend
in the capacity utilization of manufacturing industry. All periods follow specific shocks in the
economy, with quite different characteristics. The first period starts in the first quarter of
1994, favored by the introduction of a new currency and the end of a high-inflation period in
15 It is worth of note that mean absolute errors in forecast have a shock component from 1999’s currency devaluation. However, statistics after 1999 show a difference in magnitude, but not in qualitative results.
the Real plan. The end of high inflation boosted the durable goods imports, which rose
52.59% in terms of quantity, comparing the average of the first and second quarters of 1994.
The increase in consumer’s goods imports was also supported by a major reduction of import
taxes over these goods, in order to regulate the relation between demand and supply without
prejudice of inflation control.
The adoption of a floating exchange rate regime dates the beginning of the second
period of rapid growth. In this period, the growth in imports was better distributed among
different types of goods. Indeed, along the period, there was a significant increase also in the
imports of intermediate goods. The use of import tariffs to control internal supply was not an
option of economic policy in this period. In this sense, the currency devaluation constituted a
major problem to the increase of imports, which remained, since then, with a low growth rate
of quantities.
TABLE L – Economic Expansion and Imports Description Periods of Economic Expansion
Beginning 1994-1 1999-1 2003-1 Capacity Utilization – Beginning 77.0 79.0 79.2
End 1995-2 2000-4 2004-2 Capacity Utilization – End 86.0 84.1 81.9
Number of Quarters 6 8 6
Largest Variation in Imports:
t=0 -1.28% -23.22% 0.17% Component QCAP QINT QINT
Total Variation – Imports -7.55% -26.40% -6.30% t=1 52.59% 28.83% 5.79%
Component QMD QMD QINT Total Variation – Imports 17.93% 13.22% 2.95%
t=2 35.56% 22.12% 12.73% Component QMND QINT QCAP
Total Variation – Imports 5.80% 4.62% 8.70% t=3 163.68% 19.02% 31.55%
Component QMD QMND QCAP Total Variation – Imports 53.13% 4.01% 6.23%
t=4 34.77% -2.80% 12.11% Component QMD QINT QMD
Total Variation – Imports 2.01% -10.19% -2.51% t=5 18.73% 36.28% 13.38%
Component QCAP QMD QMD Total Variation – Imports 9.63% 11.44% 7.73%
T=6 38.17% Component QMD
Total Variation – Imports 15.48% T=7 7.78%
Component QCAP Total Variation – Imports -3.08%
The third period started with the control of the confidence crises, in the end of 2002
due to the election period. The period also coincides with the external growth that boosted
world’s imports. This framework contributed with an expansion of intermediate and capital
goods imports. In both previous periods, consumption goods were always among the largest
variations in imported quantities. However, as table L shows, the imports of durable goods
only started to lead the increase in imports in the beginning of 2004.
Another important factor to sustain this period is exactly the increase in exports.
Despite the fact that the absence of supply shocks did not interrupted economic growth, like
in the previous two periods, the increase in imports was always lower then the increase in
exports. In this sense, expansion started in 2003 was the only one that had the trade balance
playing a major role in the constitution of the balance of payments’ equilibrium.
Finally, one last question that must be stressed is the need of specific reforms in order
to enter in a definitive process of long run growth and convergence towards a higher
economic development. Estimates of long run elasticities show that external sector
equilibrium can only be achieved in Brazil if the capital account compensate results from the
trade balance, supposing that, on average, Brazil’s GDP grows at the same rate as the rest of
the world. The discrepancy between world’s income elasticity demand of Brazilian exports
(1.049 in the full sample estimation and 1.565 after 1995Q3) and the GDP elasticity in the
equation of demand of imports (2.674, full sample, and 1.775 after 1994Q2) justify this point.
5. Conclusions:
Some general results must be stressed in the ECM models estimated in this
paper. The first addresses directly to the usefulness of economic policy’s instruments
to achieve external sector equilibrium in the short run through imports and exports.
While, on the one hand, imports react very fast from exogenous shocks, the same do
not occur for exports. In this sense, instruments that affect both trade flows, like the
real effective exchange rate, will not offer in the short run the same response from
them. Exports need a specific set of policies in order to stimulate domestic
productivity, instead of short run measures.
Comparing with the literature, our results agree that Brazil do not suffer with a
“trade elasticity pessimism”, since estimations do not disagree from international
experience. On the other hand, the hypothesis of a constraint in exports offer can not
be supported here, since the industry’s capacity utilization is not an obstacle to
expand exported quantities.
Another important feature is related with long run income elasticities for
imports and exports. Even considering as “true” parameters those associated with the
estimated cointegration with the full sample, the significant difference among them
reinforces the need of a complete agenda to setup a higher level of exports as a trend.
Periods with Brazil sustaining a higher economic growth, compared with the world’s
average, will result in external sector disequilibria, in terms of traded quantities.
6. References:
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Comercial e a Relação Câmbio-Investimento, IPEA, Texto para Discussão n. 821, September,
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CAVALCANTI, M.A.F.H. and KAI, H.M. Avaliação do Desempenho Recente das
Exportações Brasileiras – 1999-2001, Boletim de Conjuntura IPEA , Technical Note n. 55,
October, 2001.
CORSEUIL, C. H., GONZAGA, G. and ISSLER, J. V. Desemprego Regional no Brasil:
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HAMILTON, J.D. Time Series Analysis, Princeton University Press, 1994.
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ECONOMETRIC SOCIETY, LAMES, ANNALS…, CD-ROM, July, 2004.
PAIVA, C. Trade Elasticities and Market Expectations in Brazil, IMF Working Paper,
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PERRON, P. The Great Crash, the Oil Price Shock and the Unit Root Hypothesis,
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Appendix A: Description of variables
Indexes of Volume — LQX, LQXS, LQXMAN, LQXB, LQM, LQMND, LQMD, LQCAP, LQINT
Natural logarithm of the traded quantities indexes’ quarterly average, published monthly from FUNCEX.
Indexes of Prices — LPX, LPXS, LPXMAN, LPXB, LPM, LPMND, LPMD, LPCAP, LPINT
Natural logarithm of the traded prices indexes’ quarterly average, published monthly from FUNCEX.
Real Effective Exchange Rate — REER Nominal R$/US$ exchange rate, deflated by the Producer Price Index from Brazil and United States.
World’s Real Imports – LWM World’s total imports, net of Brazilian imports, divided by the World’s import price index. Source: IFS-IMF.
Capacity Utilization in Manufacturing Industry – LNCU
Natural logarithm of the level of capacity utilization in manufacturing industry, calculated by FGV.
Gross Domestic Product – Brazil – LGDP Natural logarithm of the gross domestic product of Brazil, calculated by IBGE.
Appendix B: Statistics of estimated error-correction terms
Dependent Variable Hypothesis Mean Std. Error PP Unit Root Test
Full-Sample 2.10438E-16 0.169051 -3.032701* LQX
With Structural Break -1.28376E-15 0.137206 -4.002466*
Full-Sample -6.90357E-16 0.235234 -4.296196* LQXS
With Structural Break 1.45654E-16 0.141323 -5.871285*
Full-Sample 9.43185E-16 0.211546 -2.991214* LQXMAN
With Structural Break 1.06279E-15 0.127096 -4.445807*
Full-Sample -4.44089E-17 0.166005 -4.243186* LQXB
With Structural Break 7.30729E-16 0.143991 -5.263354*
Full-Sample 1.81898E-15 0.297448 -2.161452 LQM
With Structural Break 2.50149E-15 0.148567 -5.626752*
Full-Sample 2.83985E-15 0.382633 -4.166191* LQMND
With Structural Break -2.00067E-15 0.299696 -5.396655*
Full-Sample -1.55658E-15 0.687901 -2.419738 LQMD
With Structural Break -1.23087E-15 0.483092 -3.625517*
Full-Sample 1.75540E-15 0.296044 -5.115228* LQCAP
With Structural Break 4.80341E-16 0.248690 -6.152329*
Full-Sample -1.07114E-15 0.161500 -4.392931* LQINT
With Structural Break 2.47053E-15 0.138894 -4.607454*
Note: (*) indicates stationarity at 5%.