STRUCTURAL BREAKS AND TRADE ELASTICITIES IN BRAZIL: A TIME-VARYING COEFFICIENT APPROACH

Transcription

1 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. angelo.fasolo@bcb.gov.br. 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.

2 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. Exports Forecast Table A Market Expectations and Trade Balance Realized Imports Forecast Realized Trade Balance Forecast Realized 12-Months-Ahead Expectations Months-Ahead Expectations NOTE: Inflation expectations are available at the Central Bank of Brazil s website, on 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 substitution 1. 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

3 the new regime implied a nominal exchange rate devaluation of 22.7% in 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 Similar results are presented in the Inflation Report of December 2003.

4 Unit roots tests, under the null hypothesis of structural breaks in deterministic components, have problems in the identification of non-stationary processes 2. 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 equation 3, 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 errorcorrection 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 verified 4. 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.

5 The basic model structure adopted is an ECM 5 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: y t = c t t m n yt i + β t ( L) xt i + χ t ( yt 1 θxt 1 ε t i= 1 i 1 + α ( L) ) + (1) where y t is the endogenous variable, x t is a vector of exogenous variables and ε t 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, x t, 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, c t, and seasonal factors as deterministic terms. All coefficients, including vectors α t, β t and χ t are time-varying, estimated by the Kalman filter 6. We assume that every parameter follows a random walk as stochastic process. Supposing that υ i variables are white noise and uncorrelated among them, we have: c t = c α = α β = β χ = χ with : t 1 t 1 υ ~ iid i t t t t 1 t 1 + υ c + υ α + υ + υ β χ 2 ( 0, σ ), i i = c, α, β, χ 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 (2) 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.

6 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 overestimated in out-of-sample exercises. This result may be a consequence of the assumed hypothesis, especially about domestic GDP 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 results 8. 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 Traditional ADF and PP tests are available with the author. Description of variables presented in the appendix A of this paper.

7 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 Imports - Prices Export - Prices LOG(Índex) LOG(Index) Period M - bens de capital - preços M - consumo duráveis - preços M - consumo não duráveis - preços M - intermediários - preços M - preços Period X - básicos - preços X - manufaturados - preços X - semi-manufaturados - preços X - preços Imports - Quantum Exports - Quantum LOG(Index) LOG(Index) - Consumer Durable LOG(Index) Períod M - bens de capital - quantum M - intermediários - quantum M - quantum M - consumo duráveis - quantum M - consumo não duráveis - quantum Period X - básicos - quantum X - manufaturados - quantum X - semi-manufaturados - quantum X - quantum

8 About unit root tests applied in other variables, some common results in the literature 9 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 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.

9 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 (t) 1995: :01-15,60263* emi-manufactured goods Exports Volume LQXS (t) 1991: (t) 1993:04-13,38384* Manufactured goods Exports Volume LQXMAN (t) 1984: (t) 1984:03-12,85234* Basic goods Exports Volume LQXB (t) 1997: (t) 1998:04-23,03229* Total Imports Volume LQM (t) 1994: :02-11,35088* Non-Durable Goods Imports Volume LQMND (t) 1994: (t) 1994:02-11,47738* Consumer Durable Goods Imports Volume LQMD (t) 1993: (t) 1993:01-10,06614* Capital Goods Imports Volume LQCAP (t) 1994: :02-15,60582* Intermediate Goods Imports Volume LQINT : :03-10,93235* Total Exports Prices LPX (t) 1994: (t) 1992:04-9,367779* Semi-manufactured goods Exports Prices LPXS (t) 1993: :02-6,666389* Manufactured goods Exports Prices LPXMAN (t) 1992: (t) 1992:04-7,467081* Basic goods Exports Prices LPXB (t) 1997: (t) 1995:02-10,39538* Total Imports Prices LPM (t) 1986: * 1986:03-10,33210* Non-Durable Goods Imports Prices LPMND (t) 1992: * 1988:02-10,98044* Consumer Durable Goods Imports Prices LPMD * (t) 1990: * (t) 1990:02-11,96310* Capital Goods Imports Prices LPCAP (t) 1989: (t) 1989:02-15,19493* (t) Intermediate Goods Imports Prices LPINT (t) 1989: (t) 1986:04-8,231295* Real Effective Exchange Rate REER (t) 1991: :04-8,159108* World s Real Imports LWM (t) 1986: (t) 2002:02-23,65730* Capacity Utilization in Manufacturing Industry LNCU a * 1989: * 1990: * Gross Domestic Product Brazil LGDP (t) 1990: (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)

10 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 REER LWM Export Prices Constant Chow Test P-Value Full Sample * ( ) * ( ) ( ) ( ) Before Break Date * ( ) * ( ) ( ) * ( ) After Break Date ( ) * ( ) * ( ) * ( ) Full Sample ( ) * ( ) ( ) * ( ) Before Break Date * ( ) * ( ) ( ) * ( ) After Break Date ( ) * ( ) ( ) ( ) LQXMAN Break date: 1984:03 LQXB Break date: 1997:01 Full Sample Before Break Date * ( ) ( ) * ( ) * ( ) After Break Date ( ) * ( ) * ( ) * ( ) Full Sample * ( ) * ( ) * ( ) ( ) Before Break Date ( ) * ( ) * ( ) ( ) REER * ( ) LWM * ( ) Export Prices * ( ) Constant * ( ) Chow Test P-Value Note: (*) indicates significance at 5%. Standard errors are in parenthesis. After Break Date ( ) * ( ) ( ) * ( ) Analyzing the ECM representation, one important issue is the significance of estimated variance coefficients (the σ 2 i 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

11 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 World s Real Imports Export Prices Autoregressive Cointegration Coefficients Variance Significance Test ( σ 2 i = 0) Joint Significance (0.6254) (0.3233) (0.0220) (0.1846) Constant + Seasonal 6.58E ( ) ( ) ( ) ( ) Real Effective Exchange Rate World s Real Imports ( ) ( ) ( ) ( ) Export Prices ( ) ( ) ( ) Autoregressive Cointegration ( ) ( ) 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 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.

12 repeated in the price elasticity of manufactured goods, which presents a striking change in GRAPH 2 Short Run Time-Varying Elasticities Exports Q2 World's Real Income Elasticity Export Prices' Elasticities Q1 1978Q1 1979Q1 1980Q1 1981Q1 1982Q1 1983Q1 1984Q1 1985Q1 1986Q1 1987Q1 1988Q1 1989Q1 1990Q1 1991Q1 1992Q1 Period 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1 1999Q1 2000Q1 2001Q1 2002Q1 2003Q1 2004Q Q1 1978Q1 1979Q1 1980Q1 1981Q1 1982Q1 1983Q1 1984Q1 1985Q1 1986Q1 1987Q1 1988Q1 1989Q1 1990Q1 1991Q1 1992Q1 Period 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1 1999Q1 2000Q1 2001Q1 2002Q1 2003Q1 2004Q1 Total Exports Basic Goods - Exports Semi-manufatured Goods - Exports Manufactured Goods - Exports 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 industry 11. 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 nonnested models.

13 about the relation between economic activity and exports, since all estimates do not present any significance. Constant Seasonal 1 Seasonal 2 Seasonal 3 TABLE E Trade Elasticities Volume of Exports 2004Q2 LQX LQXS LQXMAN LQXB * ( ) ( ) * ( ) ( ) D(REER t-1 ) ( ) ( ) ( ) ( ) ( ) ( ) * ( ) * ( ) ( ) ( ) D(REER t-2 ) D(LWM t-1 ) ( ) ( ) * ( ) * ( ) * ( ) * ( ) * ( ) ( ) ( ) D(LWM t-2 ) D(Export Prices t-1 ) ( ) ( ) D(Export Prices t-2 ) ( ) ( ) AR(1) ( ) ( ) ( ) AR(2) * ( ) ( ) ( ) Cointegration * * ( ) ( ) ( ) 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.

14 Variance of parameter in time Significance test Var(REER) Final State 2004 Q2 TABLE F Alternative Models Volume of Exports LQX LQXS LQXMAN LQXB Model with Real Exchange Rate Volatility ( ) ( ) ( ) ( ) Model with Real Exchange Rate Volatility and Capacity Utilization in Manufacturing Industry Variance of parameter along time 2.827E-07 Significance test Var(REER) ( ) Final State 2004 Q2 ( ) ( ) ( ) ( ) Variance of parameter along time 2.755E-06 Significance test LNCU ( ) Final State 2004 Q2 ( ) ( ) ( ) ( ) Note: values in parenthesis express p-values. Complete estimations are available with the author 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 vector 12. 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.

15 REER LGDP Import Prices Constant Chow Test P-Value REER LGDP Import Prices Constant Chow Test P-Value REER LGDP Import Prices Constant 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 * * * ( ) ( ) ( ) ( ) ( ) ( ) * ( ) ( ) ( ) * ( ) * ( ) * ( ) * ( ) * ( ) ( ) * ( ) ( ) * ( ) * ( ) ( ) ( ) * ( ) * ( ) * ( ) 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 * * * * * ( ) ( ) ( ) ( ) ( ) ( ) * * * * ( ) ( ) ( ) ( ) ( ) ( ) * ( ) ( ) ( ) * ( ) * ( ) * ( ) * ( ) ( ) ( ) LQINT Break date: 1983:02 Full Sample Before Break Date After Break Date * * ( ) ( ) ( ) * * ( ) ( ) ( ) * * ( ) ( ) ( ) * * ( ) ( ) ( ) Chow Test P-Value 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.

16 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 GDP Capacity Utilization Import Prices Autoregressive Cointegration Coefficients Variance Significance Test ( σ 2 i = 0) Joint Significance ( ) ( ) ( ) ( ) (6.2759E-05) Constant + Seasonal ( ) (6.3838E-14) ( ) ( ) ( ) Real Effective E Exchange Rate ( ) ( ) ( ) GDP E ( ) ( ) ( ) ( ) Capacity Utilization 4.32E E E ( ) ( ) ( ) ( ) ( ) Import Prices (3.5390E-09) ( ) ( ) Autoregressive E ( ) ( ) ( ) ( ) ( ) Cointegration E E ( ) ( ) ( ) ( ) ( ) 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 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 growth 13. 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%; period had an average growth of 0.84%; after 1999, the average growth of GDP per year was of 1.63%, until 2003.

17 GRAPH 3 Short Run Time-Varying Elasticities Imports Q2 Imports' Income Elasticity Real Effective Exchange Rate Elasticity Q1 1981Q1 1982Q1 1983Q1 1984Q1 1985Q1 1986Q1 1987Q1 1988Q1 1989Q1 1990Q1 1991Q1 1992Q1 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1 1999Q1 2000Q1 2001Q1 2002Q1 2003Q1 1980Q1 1981Q1 1982Q1 1983Q1 1984Q1 1985Q1 1986Q1 1987Q1 1988Q1 1989Q1 1990Q1 1991Q1 1992Q1 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1 1999Q1 2000Q1 2001Q1 2002Q1 2003Q1 Period Period Total Imports Durable Goods Intermediate Goods Capital Goods Total Imports Durable Goods Capital Goods Capacity Utilization Elasticity Import Prices' Elasticities Q1 1981Q1 1982Q1 1983Q1 1984Q1 1985Q1 1986Q1 1987Q1 1988Q1 1989Q1 1990Q1 1991Q1 1992Q1 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1 1999Q1 2000Q1 2001Q1 2002Q1 2003Q1 1980Q1 1981Q1 1982Q1 1983Q1 1984Q1 1985Q1 1986Q1 1987Q1 1988Q1 1989Q1 1990Q1 1991Q1 1992Q1 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1 1999Q1 2000Q1 2001Q1 2002Q1 2003Q1 Period Period Total Imports Durable Goods Non-Durable Goods Intermediate Goods Capital Goods 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 widespread, followed by a period where internal production substitutes foreign trade 14. 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 nondurable 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.

18 GRAPH 4 Cross Correlation: Quarterly Variation of Imports (t) and Industry s Capacity Utilization (t+i) GRAPH 5 Long-Run Adjustment Coefficient Intermediate and Non-Durable Goods Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Non-Durable Goods - Cointegration Q1 1983Q4 1984Q3 1985Q2 1986Q1 1986Q4 1987Q3 1988Q2 1989Q1 1989Q4 1990Q3 1991Q2 1992Q1 1992Q4 1993Q3 1994Q2 1995Q1 1995Q4 1996Q3 1997Q2 1998Q1 1998Q4 1999Q3 2000Q2 2001Q1 2001Q4 2002Q3 2003Q2 2004Q1 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.

19 Constant Seasonal 1 Seasonal 2 Seasonal 3 TABLE I Trade Elasticities Volume of Imports 2004Q2 LQM LQMND LQMD LQCAP LQINT ( ) ( ) * ( ) ( ) * ( ) * ( ) ( ) ( ) * ( ) ( ) ( ) ( ) ( ) * ( ) ( ) ( ) * ( ) * ( ) * ( ) ( ) D(REER t-1 ) D(REER t-2 ) ( ) ( ) ( ) D(LGDP t-1 ) * * * ( ) ( ) ( ) ( ) D(LGDP t-2 ) ( ) D(LNCU t-1 ) * * * * ( ) ( ) ( ) ( ) D(LNCU t-2 ) * ( ) ( ) ( ) D(Import Prices t-1 ) ( ) ( ) D(Import Prices t-2 ) ( ) ( ) AR(1) * * * ( ) ( ) ( ) ( ) ( ) AR(2) ( ) ( ) Cointegration * * * ( ) ( ) ( ) ( ) ( ) 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 ( ) 3.98E-15 ( ) 4.33E-09 ( ) 1.48E-11 ( ) 3.42E-06 ( ) Final State 2004 Q2 ( ) ( ) ( ) ( ) ( ) 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.

20 In order to evaluate the role of structural breaks in forecasting exercises, table K presents a small in-sample exercise starting in , 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 Number of Exports Imports Quarters Ahead 1999Q1 Time-Varying 1999Q1 Time-Varying Parameters Parameters Parameters Parameters One Quarter Two Quarters Three Quarters Four Quarters Earlier results do support the structural break hypothesis in Brazilian foreign trade, but only before 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 Indeed, the growth of the world in 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 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.