DOES CONSUMER CONFIDENCE HELP FORECASTING CONSUMPTION SPENDING IN BRAZIL? EVIDENCE FROM SURVEY DATA

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1 DOES CONSUMER CONFIDENCE HELP FORECASTING CONSUMPTION SPENDING IN BRAZIL? EVIDENCE FROM SURVEY DATA Aloisio Campelo Jr Fundaçao Getulio Vargas, Instituto Brasilero de Economia, Rua Barão de Itambi, 60 - Botafogo CEP: , Rio de Janeiro, Brasil Viviane Seda Bittencourt viviane.bittencourt@fgv.br Fundaçao Getulio Vargas, Instituto Brasilero de Economia, Rua Barão de Itambi, 60 - Botafogo ABSTRACT CEP: , Rio de Janeiro, Brasil Marco Malgarini 1 malgmarco@gmail.com ANVUR, Via Ippolito Nievo 35, 00153, Rome, Italy Confidence has often been considered one of the main determinants of financial crises, and its has been advocated as one of the possible drivers for stimulating economic recovery. Empirical evidence about the relationship between consumer confidence and household consumption is generally inconclusive; in many cases, available analysis does not seem to confirm that confidence is able to predict expenditures after controlling for the role of economic fundamentals. However, most of this kind of literature refers to Europe and the US and, generally speaking, to the most advanced economies, while evidence for emerging countries is still sparse. The main aim of this paper is to fill this gap for Brazil, looking at Consumer Survey data published by the Fundação Getulio Vargas (FGV) since 2005, and studying their relationship with household consumption expenditures. According to our results, the Consumer Confidence Index and the Present Situation Index can be good predictors of consumption and help improving the goodness of fit of consumer spending forecasting models. Especially in periods of high economic and political uncertainty, these indicators have even greater role in understanding the future behaviour of consumers. Keywords: Consumer Confidence, Household Consumption Expenditures, Survey Data, Brazil, VAR models. JEL Classification: D12, C32, E17, D8 1 Associate researcher, FGV 1

2 DOES CONSUMER CONFIDENCE HELP FORECASTING CONSUMPTION SPENDING IN BRAZIL? EVIDENCE FROM SURVEY DATA 1. INTRODUTION AND OVERVIEW Confidence has often been considered one of the main determinants of financial crises, and its has been advocated as one of the possible drivers for stimulating economic recovery. According to Acemoglu and Scott (1994), periods of economic uncertainty (such as under political crisis or war) are usually associated with greater consumer sentiment volatility and often a drop in household consumption, once controlling for standard economic determinants such as credit, inflation and income growth. This suggests that large swings in sentiment can influence consumption and that consumer confidence is a statistically significant factor in determining household consumption assuming special relevance in periods of high uncertainty. Some researchers confirmed this hypothesis (Garner, 1991 and Throop, 1992). Caroll (1994), for instance, showed that before the US recession consumer confidence was the only widely known economic indicator presenting a great decline. Bram and Ludvigson (1998) mention that the relationship between consumer confidence and household consumption expenditures has been widely analysed in economic literature 2 but generally it is inconclusive. In many cases, analysis does not seem to confirm that confidence is able to predict expenditures after controlling for the role of economic fundamentals. However, most of this kind of literature refers to Europe and the US and, generally speaking, to the most advanced economies, while evidence for emerging countries is still sparse. In Brazil, there are a few articles associating consumption and confidence. One of the empirical studies (Bentes, 2006) sought to quantify the relationship between consumer confidence and the level of consumption in Brazil, by investigating the existence of dominant causality. He shows that consumer confidence has a significant explanatory power on total consumption after controlling for the main macroeconomic variables used to explain consumers behaviour. 2 As mentioned by Bram and Ludvigson (1998), the early investigators of the explanatory power of consumer confidence include Fair (1971), who links the University of Michigan index with both durable and nondurable consumer expenditures, and Mishkin (1978), who argues that the Michigan index may be a good proxy for the consumer s subjective assessment of the probability of future financial distress. More recent work analysing the Michigan index can be found in Carroll and Dunn (1997), Carroll, Fuhrer, and Wilcox (1994), Fuhrer (1993), Leeper (1992), and Matsusaka and Sbordone (1995). 2

3 More recently, Graminho (2015) used Kalman filter to identify a pure sentiment-based indicator extracted from FGV s consumer confidence indicator after controlling for macro-based variables. She found that sentiment increases the predictive power of consumption even over a sixmonth horizon. In this paper we will use data from the FGV Consumer Survey to investigate whether measures of consumer sentiment helps to improve forecasts of household consumption. The data series for household consumption is collected by the Brazilian Institute of Geography and Statistics IBGE on a quarterly basis. Figure 1 plots the quarterly growth rate of real household consumption, measured as the amount of consumer spending within the quarter compared to the same quarter of the previous year, along with the quarterly level of the monthly CCI quarterly averaged. The graph indicates a fairly strong correlation between consumer spending growth rate and consumer confidence. While it is the highest when computed on a contemporaneous fashion (0.63), the correlation measured using CCI with a one quarter lead remains relatively high (0.57), indicating potential leading properties for consumer sentiment. Comparing with other countries this seems to be one of the highest reported correlations between consumption and confidence. In the United States the contemporary correlation is 0.11 (0.28 with one lag). In the Euro area, correlation between confidence and consumption is the highest when confidence is lagged one period (0.52). Figure 1 Brazilian: Consumption growth and changes in confidence Source: FGV/IBRE. 3

4 The aim of this paper is to empirically test consumer confidence as an explanative variable for household consumption in Brazil and if it brings additional information to models that are carefully built with variables usually included in forecasting household consumption (e.g. income, wealth or interest rates). We identified under which circumstances confidence indicators can be a good predictors of household consumption in a way that does not add methodological novelty to the empirical literature but provides international comparison with other countries. The present work is organised as follows: Section 2 presents the methodological features: data and a brief description of the Brazilian Consumer Survey. Section 3 describes the empirical framework, reporting results from both univariate and multivariate analysis. In section 4, we analyse the forecasting results. The last section presents conclusions and final considerations. 2. METHODOLOGICAL FEATURES 2.1 Data The reference variable is the Household Consumption Expenditures series, as published by the Brazilian Institute of Geography and Statistics (IBGE), National accounts. The Consumer Survey Database covers the period between the third quarter of 2005 and the fourth quarter of Although it is not a regular practice in Brazil (and not consensual at the international level) we have decided to use the reference series and other variables after being seasonally adjusted, following Golinelli (2013). Concerning confidence indicators, we have tested the Brazilian Consumer Confidence Index (CCI) and the Present Situation Index (PSI) both taken from FGV s Brazilian Consumer Survey. PSI is a sub component of the CCI. We have used it as an alternative that could provide data related to agents' perceptions that should be less influenced by non-objective information. Following Dées and Brinca (2013), we checked if there was some retro-action of foreign confidence by testing the influence of the US Michigan University Confidence indicator and the Euro Area Eurostat CCI on Brazilian consumption spending. Among the variables that comprise the study, we chose the explanatory variables most widely mentioned in the empirical literature as having predictive power in explaining changes in consumption such as: real disposable income, interest rates, unemployment rate, consumer price index and the stock market index (as a proxy for wealth). In the case of Brazil, as an emerging 4

5 economy, we also decided to test a Commodity prices index. The specific series included in the basic model were selected after testing their predictive power towards household consumption. As a proxy for real disposable income, we used the real average labour income for private sector employees 3 perceived in the reference month by people over 10 years of age (Monthly Employment Survey, PME, IBGE). Regarding interest rates, we chose the basic interest rate (SELIC), available from the Brazilian Central Bank (BCB). The unemployment rate and the consumer price index were obtained from IBGE. The inflation indicator selected was the Nation Wide Consumer Price Index (IPCA) for free-prices, i.e. all prices that are not controlled by the government. As a proxy for wealth, we have tested two stock market indices and one real state prices index. The Bovespa Index (IBOV) is the main stock market index in Brazil; the IBrX 50 is designed to measure the average performance of the 50 most traded stocks; the Central Bank s IVG-R follows real estate prices. Finally, we have tested some aggregate measures of commodity prices assuming they might have some impact in the Brazilian economy. For this task the Central Bank s Brazilian Commodities Index was chosen. All variables are seasonally adjusted though no previous cyclical adjustments were made. 2.2 FGV Brazilian Consumer Survey The FGV Brazilian Consumer Survey has been producing and publishing since It consults 2,100 people monthly, in average, mostly by telephone but also, since 2014, through a Web-based questionnaire 4. The survey aims at generating indicators to capture perceptions and expectations of the Brazilian consumers regarding topics such as general economic situation, household financial situation, labour market conditions, savings, the intent to purchase durable goods, and expectations for variables such as inflation and interest rates. 3 At constant prices of February, Inflation adjusted by the IPCA index. 4 The web-based responses were compared to telephone questionnaires and we found no significant differences in levels and changes. In either case, the proportion of web-based questionnaires has been no higher than 5% in any month. 5

6 The questionnaire contains 22 questions on average and consists mainly of qualitative questions allowing five possible answers, arranged on a Likert scale; the reference period is either the current month (for the present situation variables) or six months ahead (for the expectations). FGV s CCI includes five indicators from which two are related to current conditions and three about future conditions. Questions about current conditions are: (1) How would you rate the current economic conditions in your area? (2) How would you rate the present financial situation of your household? Questions about future conditions are: (1) Six months from now, do you think the general economic situation will improve, remain the same or worsen? (2) Six months from now, do you think your household financial situation will improve, remain the same or worsen? (3) In the next six months, comparing with the previous six months, your expenses with durable consumer goods will be higher, the same, or lower? Data obtained for the qualitative response options are expressed as balances of positive minus negative replies. As an example from question 1: the percentage of people saying that economic conditions are bad today is subtracted from the percentage of people saying they are good. For building the most aggregated index these indicators are averaged across the five questions and then converted to a base month (September, 2005). FGV then creates the most aggregated CCI index, comprised of all five questions; the PSI (Present Situation Index), including two questions related to current conditions; and the EI (Expectations Index), published as the average of the three expectation components. The reference population for the survey is that of adult Brazilian consumers living in seven of the major capitals of the country (Rio de Janeiro, São Paulo, Belo Horizonte, Porto Alegre, Brasília, Recife and Salvador).The 2,100 consumers contacted each month generate results with an absolute sampling error of 2.2 percentage points at a 95% confidence interval. The sample is stratified according to income and region of residence of the respondent and is proportional to the participation of each stratum to total households consumption in these cities. Some results may be available with acceptable statistical significance for other types of socio-demographic respondent characteristics. More specifically, on the basis of the questionnaire it is possible to distinguish survey participants according to their income (four classes), area of residence, education (six levels), age (four levels) and gender. 6

7 3 CONFIDENCE AND CONSUMPTION CONTROLLING FOR ECONOMIC FUNDAMENTALS 3.1 Preliminary exercises: Unit root and Granger causality tests Dynamic Granger causality can be captured from the vector error correction model derived from a long-run cointegrating relationship (Granger, 1988 ). The original Granger causality proposed by Granger (1969) had potential specification bias that could lead to spurious regressions in specific cases. Engel and Granger (1987) have defined X and Y as being cointegrated if some linear combination of X and Y is stationary even if each variable is non stationary. Engel and Granger (1987) and Yoo (2005) also pointed out that while these two variables are non-stationary and cointegrated, the standard Granger -causal inference will be invalid. To mitigate these problems, we followed the Toda and Yamamoto (1995) methodology based on an augmented VAR modeling, introduced as a modified Wald test statistic. This procedure has been found to be superior to ordinary Granger-causality tests since it does not require pre-testing for cointegrating properties of the system thus avoiding potential bias associated with unit roots and cointegration tests as it can be applied regardless of whether a series is I(0), I(1) or I(2), noncointegrated or cointegrated of an arbitrary order. Toda and Yamamoto demonstrated that this method avoids the low power (Chocrane, 1986; Perron, 1989; and Kwiatkowski, 1992) of the unit root and co-integration tests, and allows testing series for causality in the Granger sense, i.e using the variables in level of our dataset. Briefly, four steps are needed to analyse the proposed test: 1) definition of maximum integration order (d); 2) definition of an optimal number of mismatches (w); 3) estimation of a VAR model with (p + d) lags; and 4) application of the Wald test restrictions in the first p offsets in order to test the absence of Granger-causality. The main results are presented in Table 1. 5 The p-values represent the probability that the row variable does NOT Granger-cause the variable indicated in each column. Rejections of the null hypothesis at the 5% level are marked in bold. Brazilian Household consumption is Granger-caused by confidence as measured by CCI, interest rate, stock market (1 and 2), and real state index. We have also performed Granger causality tests against the CCI. In Brazil, we reject the hypothesis that stock market variables, the Commodities Index and European confidence do not 5 In the causality tests, we use all the period available of the series. 7

8 granger cause Brazilian confidence (condition in which from now on we assume as being the same as saying that variable X granger causes variable Y). In the United States (Deés and Brinca, 2011), confidence is Granger-caused by financial wealth and equity prices; while in the Euro area, unemployment rate, interest rates and foreign confidence were the only variables that Granger caused domestic confidence. Table 1: Granger causality tests (Toda e Yamamoto 1995) Dependent variables Independent variables ln Consumption ln Brazilian CCI ln Consumption ln Brazilian CCI 0.00 ln real income ln interest rate ln stock market ln stock market ln real state index ln unemployment rate ln commodities index ln CCI (US) ln CCI (EU) inflation Notes: Brazilian CCI: Brazil confi dence index; consumption: real consumption expenditures; Income : real income; interest rate: Selic; stock market index 1: Ibov, stock market index 2: ibxr50, real state index: ivg_r (proxy for wealth), unemployment rate, commodities index: CI_Brazil, CCI (US): CCI Michigan, CCI (EU): CCI Europe and inflation: ipca_livres. P-values reported for the probability of Row NOT Granger-causing Column. Source: FGV/IBRE. Considering the political and economic crises that Brazil has been facing with the recession starting in , we opted to include the PSI (Present Situation Index) in our causality tests against household consumption and macroeconomics variables. As mentioned before, the hypothesis is that extreme economic volatility in the economy may lead to the increase of false signals coming from expectations. 6 As established by the Brazilian Business Cycle Dating Committee. 8

9 The results for the PSI tests are presented in Table 2. Brazilian household consumption is also Granger-caused by confidence as measured by PSI. Concerning other economic variables, there are signs that the Commodities Index and European confidence also cause PSI as it happens with CCI. But stock market indices and interest rates show non-significant p-values. The results of the Granger causality tests give us a direction but the estimation of consumption models together with a dynamic analysis are necessary to better understand the role of potential determinants of household consumption and their complex dynamic relationships. Table 2: Granger causality tests (Toda e Yamamoto 1995) Dependent variables Independent variables ln Consumption ln Brazilian PSI ln Consumption 0.82 ln Brazilian PSI 0.00 ln real income ln interest rate ln stock market ln stock market ln real state index ln unemployment rate ln commodities index ln CCI (US) ln CCI (EU) inflation Notes: Brazilian PSI: Brazil Present Situation Index; consumption: real consumption expenditures; Income : real income; interest rate: Selic; stock market index 1: Ibov, stock market index 2: ibxr50, real state index: ivg_r (proxy for wealth), unemployment rate, commodities index: CI_Brazil, CCI (US): CCI Michigan, CCI (EU): CCI Europe and inflation: ipca_livres. P-values reported for the probability of Row NOT Granger-causing Column. Source: FGV/IBRE. But before we started modelling, we have also tested the series for stationarity in order to avoid the inclusion of non-stationary time series in the regression models. As it is known, mixing stationary with non-stationary series invalidate standard tests (t, F, etc.) and may lead spurious regressions. 9

10 For that reason, we performed Augmented Dickey- Fuller (ADF) tests aiming to determine the order of integration of the variables (in levels and log transformed). Most of variables are found to be integrated of order one, I(1). Stationarity is not, however, absolutely evident in CCI (consumer confidence index) and also in PSI (present situation index) as one could expect from its design. The ADF tests do not allow the existence of a unit root to be rejected at the 5% threshold. Our main concern related at this point had connection with the discussion about the stationarity of survey series. Some authors do assume confidence series to be "stationary by their nature" (see discussion in Gayer, 2010). The methodological notes of the European Commission regarding this subject state that finding the result I(1) for CCI can reflect the fact that the series used are not available for long enough time periods. Unit root tests are asymptotic tests, and so it is not surprising that a limited number of observations can lead to the non-rejection of the process integration. As we are dealing with relatively short time series and an emerging economy, for the case of this study we preferred to differentiate the Survey whenever the appropriate tests suggest the presence of a trend. 3.2 Estimating models for consumption First, we estimated a benchmark univariate model considering that changes in consumption (Δln Ct) depend only on changes in its own previous values, using a standard autoregressive model (AR): Δ ln C t = + β i C t-i + ϵ i (1) Where ϵ i is the error term. The lag order (q) was determined using Standard Information Criteria (SIC) and Akaike Criteria; one lag was found to be optimal from all models estimated. Note that these results were obtained for the period starting in 1996q1 and ending in 2015q4. The next step was producing residual and stability tests. Bentes (2006) suggested testing whether series would have some structural break that could make an otherwise stationary series appear to have an unit root. But we could not reject the null hypothesis that there were no breaks at 10

11 specific points. We found no residual autocorrelation (BG and ARCH tests) but we could not reject the heteroscedasticity hypothesis (White test) 7. Then we estimated a bivariate model by including confidence indicators. We tested both the CCI (Consumer Confidence Index) and the PSI (Present Situation Index): Δ ln C t = + β i ln C t-i + β i ln conf t-i + ϵ i (2) For this model, we also tested the lagged consumer confidence (-1 to -4). The best fit was obtained with the previous values of confidence indicators on the 1 st and 4 th previous quarters. The AR components could explain 13% of the variation of Brazilian consumption changes 8. By including confidence indicators the explanatory power rises to 37% with the CCI and 39% with the PSI. Table 3 shows the main results. We hence move a step forward and, following again Dées and Brinca (2013), add sets of other potential explanatory variables. The first set of variables, Z1, includes only past changes in consumption and changes in real disposable income (Δln Y t ); Z2 also includes changes in variables we consider as a proxy of wealth, i.e. stock market indexes and the changes in short-term interest rates (Δln Selic t ). We chose to include only the Bovespa Index because the results were not so different and it has longer series (Δln Ibov t ); the third set, Z3, includes other variables that are known to be of potential influence on consumption behaviour. These variables are the log-changes in unemployment rate (Δln U t ), inflation (Δln CPI t ) and the Commodities Price Index (Δln Ci t ). For each set of variables, we compare the R 2 of the model (Eq. 3) with that of an alternative version that also includes changes in confidence indicators (Eq. 4). Δ ln C t = + β i ln C t-i + i Z k t-i + ϵ i, for k= 1,2,3. (3) Δ ln C t = + β i ln C t-i + i Z k t-i + β i ln conf t-i + ϵ i, for k= 1,2,3. (4) 7 When testing the consumption expenditures series using all period available ( ) we rejected the heteroscedasticity hypothesis. 8 When testing for a longer period ( ) R 2 decreases to 4%. 11

12 As shown in Table 3, the inclusion of selected macroeconomic variables increase the explanatory power of the regression. The set of fundamentals Z1 explain 39% of the linear variation in consumption expenditures changes, while set Z2 explains 47%. Using set Z3, R 2 decreases to 45%. Dées and Brinca (2013) find relatively similar results for the United States with 18%, 35% and 42% of explaining power respectively. Meanwhile in the Euro area they found numbers surprisingly lower: 9%, 7% and 16% respectively. Most importantly, in each case the addition of confidence indicators contributes to further increase the explanatory power of the regression. When we use the CCI in particular, once Z1 and Z2 are considered, R 2 rises respectively to 0.54 and With set Z3 it decreases a bit, to Comparing the results obtained with the inclusion of the CCI, we found out that PSI improves the regression results. In this way, once considering PSI with Z1 and Z2, R 2 increases to 0.58 and 0.65, respectively; decreasing to 0.61with set Z3. Finally, for the last model we also included, besides information on past values of consumption and the set of fundamentals Z3, foreign confidence indexes (Z3*): in this case, the idea it to test if adding US and Euro confidence indicators improve the model s fitness: Δ ln C t = + β i ln C t-i + i Z 3 t-i + i Z 3* t-i + ϵ i (5) Δ ln C t = + β i ln C t-i + i Z 3 t-i + i Z 3* t-i + β i ln conf t-i + ϵ i (6) The inclusion of foreign variables to the model do not seem to improve its explicative power. We may hence conclude that changes in US and Euro Confidence indicators do not impact the Brazilian consumption cycles. This result does not support findings by Fei (2011), that there is empirical evidence of the existence of a confidence channel from large countries to smaller countries. The first relevant results at this stage is that Brazilian confidence represented by both CCI and the PSI seem to add explanation of consumption in Brazil. In fact, the inclusion of PSI increases the goodness of fit if we consider the estimation of consumption. 12

13 Table 3: Univariate and multivariate specifications Equation Δln C t = + β i C t-i Δln C t = + β i C t-i + β i conf t-i Δln C t = + β i C t-i + i Z 1 t-i Δln C t = + β i C t-i + i Z 1 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 2 t-i Δln C t = + β i C t-i + i Z 2 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 3 t-i Δln C t = + β i C t-i + i Z 3 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 3 t-i + i Z 3* t-i Δln C t = + β i C t-i + i Z 3 t-i + i Z 3* t-i + β i conf t-i Notes = Z 1 t = ( ln C t, ln Yt), Z 2 t = ( ln Ct, ln Yt, ln Ibov, ln Selict), Z 3 t = (( ln Ct, ln Yt, ln Ibov, ln Selict, ln U, ln CPI, ln CI) indicate 1rst diferences. ln Ct = Household Consumption, ln Yt = disposable income, ln Ibovt = stock market index, ln Selict = interest rate, ln U = unemployment rate, ln CPI = inflation, ln CI = commodities index Source: FGV/IBRE. CCI R 2 PSI 3.3 Forecasting out of sample Based on models that include past values of consumption, confidence indicators and a set of fundamental variables, we perform out of sample analysis, by estimating the model for the period 2005q3 to 2013q4 and then using it to forecast consumption in the period 2014q1-2015q4. Root mean square errors are computed and used as comparison between models. As it is shown in table 4, the model that produces the smaller forecast error is the one which includes past values of consumption, disposable income, the variables proxies of wealth (stock market index and interest rate) and a confidence indicator (CCI or PSI). 13

14 Table 4: RMSE of models ( ) Equation Δln C t = + β i C t-i Δln C t = + β i C t-i + β i conf t-i Δln C t = + β i C t-i + i Z 1 t-i Δln C t = + β i C t-i + i Z 1 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 2 t-i Δln C t = + β i C t-i + i Z 2 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 3 t-i Δln C t = + β i C t-i + i Z 3 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 3 t-i + i Z 3* t-i Δln C t = + β i C t-i + i Z 3 t-i + i Z 3* t-i + β i co CCI RSME Notes = Z 1 t = ( ln C t, ln Yt), Z 2 t = ( ln Ct, ln Yt, ln Ibov, ln Selict), Z 3 t = (( ln Ct, ln Yt, ln Ibov, ln Selict, ln U, ln CPI, ln CI) indicate 1rst diferences. ln Ct = Household Consumption, ln Yt = disposable income, ln Ibovt = stock market index, ln Selict = interest rate, ln U = unemployment rate, ln CPI = inflation, ln CI = commodities index Source: FGV/IBRE. PSI The best results are obtained with set Z2 and CCI or by combining set Z3 with the PSI. If we were to choose the best model to forecasting Brazilian economic events in the near future PSI would eventually be chosen for inclusion in the model because expectations became have become more volatile and tending to produce false signals as a consequence of an unprecedented crisis in terms of scale and duration in Brazil 9. Picchetti (2016), for instance, shows that during 2015 there was a structural break between Manufacturing Survey production expectations seriesand industrial physical production. Although it is not possible to test all possibilities for this paper it looks like the increase in economic uncertainty and volatility brought by the Brazilian recession suggest that at least starting from 2016.Q1 onwards the use of PSI would produce better forecasting results. As an alternative means for selecting one of the two consumer survey variables that present similar forecasting properties for the period, we decided to test their forecasting 9 According to the Brazilian Dating Committee recession started in 2014.Q2. Until 2016.Q2 it has not ended yet. During these 9 quarters, the seasonally adjusted GDP index fell 8%. Some people claim it might be the worse recession ever in Brazil (or at least since 1900). 14

15 capabilities in a pair of years that do not include the recent Brazilian recession: In this case we avoid this atypical recessionary period accepting the burden of losing some degrees of freedom in an analytical period that was already not so large. So in either case we must be careful with drawing permanent conclusions for the CCI x PSI discussion. The tests shows that the smaller forecasting error for the period is obtained with amodel that includes past values of consumption and the PSI. Table 5: RMSE of models ( ) Equation Δln C t = + β i C t-i Δln C t = + β i C t-i + β i conf t-i Δln C t = + β i C t-i + i Z 1 t-i Δln C t = + β i C t-i + i Z 1 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 2 t-i Δln C t = + β i C t-i + i Z 2 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 3 t-i Δln C t = + β i C t-i + i Z 3 t-i + Sβ i conf t-i Δln C t = + β i C t-i + i Z 3 t-i + i Z 3* t-i Δln C t = + β i C t-i + i Z 3 t-i + i Z 3* t-i + β i co CCI RSME Notes = Z 1 t = ( ln C t, ln Yt), Z 2 t = ( ln Ct, ln Yt, ln Ibov, ln Selict), Z 3 t = (( ln Ct, ln Yt, ln Ibov, ln Selict, ln U, ln CPI, ln CI) indicate 1rst diferences. ln Ct = Household Consumption, ln Yt = disposable income, ln Ibovt = stock market index, ln Selict = interest rate, ln U = unemployment rate, ln CPI = inflation, ln CI = commodities index Source: FGV/IBRE. PSI 3.4 VAR analysis We continue the analysis by setting up VAR models and testing the dynamic impacts of a shock to confidence on household consumption, through impulse response functions. We estimated these models using the variables that present the lower RSME in the estimations from section 3.3. We thus included past changes in consumption and changes in real disposable income, and Z2 variables, changes in variables we consider as a proxy of wealth, i.e. Stock market index (IBOVESPA) and the changes in short-term interest rates (Δln Selic t ). 15

16 This allows us to test the statistical significance of the confidence indicators through the confidence intervals of the impulse response functions. We estimate the following VAR model, using the set of fundamentals (Zt), as defined above: y t q i 1 A y i t i u t where y ln Ct ln Conft Zt and μi is a vector of generalised shocks. The generalised impulse responses are invariant to the reordering of the variables in the VAR. In contrast [to the orthogonalized impulse responses], the generalized impulse responses are unique and fully take account of the historical patterns of correlations observed amongst the different shocks. (Pesaran, 1998) The optimal lag order (q) according to standard information criteria found is 3. Figure 2 shows the impulse response functions of a shock to confidence on household consumption considering 2SE standard bands around the impulse responses obtained by Analytic (asymptotic) method. The impulse response appears to be significant for three periods ahead but no longer significant for future periods at the 95% level. Figure 2 - Responses to a 1s.d. innovation in confidence (CCI) on consumption growth with 95% error bounds..015 Response of DLNBRAZIL_CF_SA to DLNC_ICC_SA Source: FGV/IBRE. 16

17 Figure 3 - Responses to a 1s.d. innovation in confidence (PSI) on consumption growth with 95% error bounds. Response of DLNBRAZIL_CF_SA to DLNC_ISA_SA Source: FGV/IBRE. In Figure 3, we also show that a shock to PSI has also significant impact on consumption lasting for around three quarters. Both figures of impulse response shocks have the same format, direction and persistence of the impact of one variable through the other. The most important is that both CCI and PSI show a positive impact in consumption since the first period persisting for the following two quarters. Standard deviation (s.d.) is used as the impulse response variable in the exercise. In the case of the PSI, for example, considering a s.d. of the response reaches in the first period remaining around this value for the two following quarters. Thus, a variation of 11.4% in PSI generates a 1% impact in Brazilian household consumption. 17

18 CONCLUSION The relationship among consumer confidence and household consumption expenditures has been often studied in the literature, but evidence for developing countries in general, and for the Brazilian economy in particular, is still sparse. The aim of this paper is to fill this gap, using Consumers Confidence Indicators (CCI and PSI) derived from FGV s Brazilian Consumer Survey. After having introduced the survey and tested the stochastic properties of the data, we have estimated a set of models considering past values of consumption and various set of explanatory variables, and then tested whether the inclusion of confidence indicators improve the fitness of the models both in and out of sample. Our main conclusion is that consumer confidence (both CCI and PSI) can be good predictors, both in and out of sample, of consumption expenditures in Brazil, adding information to other economic statistics traditionally used for forecasting consumer spending. Further research is however advisable in the future, first of all in order to check whether this relationship is particularly strong in periods of economic uncertainty, and checking for the predictive power of Consumer sentiment not only at the aggregate level but also for specific categories of consumption expenditures. Also future research may include other variables from the Consumer and the Trade Tendency Surveys. 18

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