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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.

mailto:[email protected]

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

http://www.bcb.gov.br/

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

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01

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01

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01

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01

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01

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01

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01

2001

01

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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

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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

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01

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01

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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

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01

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01

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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


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


Date Full

Sample Before


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

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1987

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1988

Q1

1989

Q1

1990

Q1

1991

Q1

1992

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1993

Q1

1994

Q1

1995

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1996

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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


Date Full

Sample Before


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


Date Full

Sample Before


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

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89Q

1

19

90Q

1

19

91Q

1

19

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1

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93Q

1

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1

19

95Q

1

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1

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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

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1

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1

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1

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1

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1

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1

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1

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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

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86Q

1

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87Q

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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

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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

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89

Q1

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89

Q4

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90

Q3

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91

Q2

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92

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92

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93

Q3

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94

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95

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95

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96

Q3

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97

Q2

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98

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98

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99

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00

Q2

20

01

Q1

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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:

BANCO CENTRAL DO BRASIL, Inflation Report, December 2003.

CAVALCANTI, M.A.F.H. and FRISCHTAK, C.R. Crescimento Econômico, Balança

Comercial e a Relação Câmbio-Investimento, IPEA, Texto para Discussão n. 821, September,

2001.

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:

uma Abordagem Empírica, In: XVIII ENCONTRO BRASILEIRO DE ECONOMETRIA, SBE,

ANNALS..., v.1, December, 1996.

ENGLE, R. and GRANGER, C. Cointegration and Error Correction: Representation,

Estimation and Testing, Econometrica, 55, 251-276, 1987.

GREENE, W.H. Econometric Analysis, fourth edition, Prentice Hall, 2000.

HAMILTON, J.D. Time Series Analysis, Princeton University Press, 1994.

HARVEY, A.C. Forecasting, Structural Time Series Models and the Kalman Filter,

Cambridge University Press, 1991.

JOHANSEN, Søren Estimation and Hypothesis Testing of Cointegration Vectors in

Gaussian Vector Autoregressive Models, Econometrica, 59, 1991.

MORAIS, I.A.C. and PORTUGAL, M.S. Structural Change in the Brazilian Demand

for Imports: a Regime Switching Approach, In: LATIN AMERICAN MEETING OF THE

ECONOMETRIC SOCIETY, LAMES, ANNALS…, CD-ROM, July, 2004.

PAIVA, C. Trade Elasticities and Market Expectations in Brazil, IMF Working Paper,

WP/03/140, July, 2003.

PERRON, P. The Great Crash, the Oil Price Shock and the Unit Root Hypothesis,

Econometrica, 57, p.1361-1401, 1989.

PERRON, P. Further Evidence on Breaking Trend Functions in Macroeconomic

Variables, Journal of Econometrics, n. 80, p. 355-385, 1997.

RIBEIRO, F.J. and MARKWALD, R. Inovações na Pauta de Exportações Braileiras,

FUNCEX, Nota Técnica year I, n. I. August, 2002.

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


Full-Sample -4.44089E-17 0.166005 -4.243186* LQXB


Full-Sample 1.81898E-15 0.297448 -2.161452 LQM


Full-Sample 2.83985E-15 0.382633 -4.166191* LQMND


Full-Sample -1.55658E-15 0.687901 -2.419738 LQMD


Full-Sample 1.75540E-15 0.296044 -5.115228* LQCAP


Full-Sample -1.07114E-15 0.161500 -4.392931* LQINT


Note: (*) indicates stationarity at 5%.

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