Forecasting, nowcasting and backcasting Brazilian GDP

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1 Forecasting, nowcasting and backcasting Brazilian GDP Pedro G. Costa Ferreira FGV IBRE Guilherme Branco Gomes FGV EPGE Daiane Marcolino de Mattos FGV IBRE Tiago dos Guaranys Martins FGV EPGE Abstract GDP is one of the most important variables used by the Central Bank and market to make monetary-policy and investment decisions. However, it is only released around 60 days after the end of the quarter in question. In this article we use the methodology developed by Giannone et al. (2008) and implemented by us in R using the nowcasting package (Mattos et al., 2018) to forecast GDP on the last day of the period being studied. We show that a dynamic factor model in which the information contained in 128 monthly variables is summarized in only two dynamic factors can be used to forecast Brazilian GDP (on a quarterly basis). Forecasts made with the model during the four weeks prior to the release of the GDP are satisfactory and have errors similar to those of specialist forecasts (Focus Bulletin). Keywords: nowcasting, GDP, dynamic factors model. 1 Introduction Monetary-policy and investment decisions are taken based on the present and future state of the economy. The variables used to describe the state of the economy are normally only available after a lag. Brazilian GDP, for example, is a quarterly variable released on average 60 days after the reference period. Real-time forecasts of key variables used to analyze the economic situation are therefore of interest to the Central Bank and the market. Giannone et al. (2008) developed a statistical model that allows quarterly variables, such as US GDP, to be forecast using a large set of monthly variables released with different lags. GDP forecasts for the current quarter are therefore updated whenever a new explanatory variable is released. This methodology was also implemented by the European Central Bank (Angelini et al., 2008; Bańbura & Rünstler, 2011; Van Nieuwenhuyze et al., 2008) and the central banks of Ireland (D Agostino et al., 2008), New Zealand (Matheson, 2010) and Norway Aastveit & Trovik (2012). The model proposed by Giannone et al. is based on dynamic factor models (DFMs). These models are designed to summarize the variation contained in a large set of data in only a few variables (Stock We are grateful to Domenico Giannone for making the Matlab code available and for his helpful comments on the bibliography. 1

2 & Watson, 2006). In Giannone et al. (2008), the authors show how to reduce the information contained in dozens of monthly time series into only two dynamic factors. The two estimated factors, which were initially monthly, are then transformed into quarterly factors and used in a regression against GDP. Various other authors, such as Chauvet (2001); Marcellino et al. (2003); Forni et al. (2004); Boivin & Ng (2006); D Agostino et al. (2006); Banbura et al. (2011); Dahlhaus et al. (2015); Stock & Watson (2016), have explored the use of DFMs in time-series forecasting. Giannone et al. (2008) and Banbura et al. (2011) introduced the terms nowcasting and backcasting, which we have adopted here and which help to specify forecasting according to the time when the forecast is made. Forecasting corresponds to forecasting the value of a variable before the period in question. Nowcasting corresponds to forecasting the value of the variable in the present (during the period in question), and backcasting corresponds to forecasting the variable after the period in question and before the value of the variable has been released. By way of illustration, suppose we want to forecast the GDP for the 2nd quarter of If the current period is a date in the 2nd quarter of 2017, then the forecast is classified as nowcasting. However, if the current date is before the 2nd quarter of 2017, then the term used is forecasting. Finally, if the date is after the 2nd quarter of 2017 and the GDP has not yet been released, then the forecast is classified as backcasting. The aim of this article was to develop a model for forecasting Brazilian GDP following the method proposed by Giannone et al. (2008). To encourage use of the methodology, we developed the model in R 1. The model estimation functions were only translated to R as Giannone et al. (2008) and Banbura et al. (2011) supplied the code in Matlab. Other functions were created to make it easier to treat the time series and analyze the results. The article is organized as follows: section 2 gives a brief introduction to the theory underlying dynamic factor models and their estimation according to Giannone et al. (2008) and provides an overview of the database; section 3 presents the results obtained using the methodology and an analysis of these. Lastly, some final considerations are presented in section 4. Although the methodology is used here to forecast GDP, it can also be applied to other time series. 2 Methodology 2.1 Dynamic Factor Model (DFM) Let x t = (x 1,t, x 2,t,..., x N,t ) be the vector representing N monthly time series transformed to satisfy the assumption of stationarity. The general specification of the dynamic factor model (DFM) is given by: x t = µ + Λf t + ε t (1) p f t = A i f t i + Bu t, u t NID(0, I q ) (2) i=1 where µ is the vector of unconditional means; f t is the vector of (unobserved) common factors of 1 Access to find out more about the nowcasting package 2

3 dimension r 1 modeled by a VAR(p) process in which the matrices A i of dimensions r r represent the coefficients of the autoregressive process and matrix B is a matrix of coefficients of dimensions r q; Λ is a matrix of constants of dimensions N r; and ε t is a vector of idiosyncratic components such that Ψ = E[ε t ε t]. We assume also that E[ε t u t k ] = 0 for any k. In the so-called exact dynamic factor model the error components are assumed to be mutually uncorrelated at all lags, i.e., E[ε i,t ε j,s ] = 0 for i j. However, the idiosyncratic component may follow an AR(p) process as shown in equation (3). This procedure can be found in greater detail in Banbura et al. (2011). q = 2. ε i,t = where E[e i,t e j,s ] = 0 for i j. p α i ε i,t i + e i,t, e i,t NID(0, σi 2 ) (3) i=1 In the nowcasting package both structures can be considered for ε t. Following is an example in matrix form of equation 2 of the model for orders r = 2, p = 2 and f 1,t f 2,t f 1,t 1 f 2,t 1 a 1 1,1 a 1 1,2 a 2 1,1 a 2 1,2 f 1,t 1 b 1,1 b 1,2 = a 1 2,1 a 1 2,2 a 2 2,1 a 2 [ ] 2,2 f 2,t f 1,t 2 + b 2,1 b 2,2 u 1,t 0 0 (4) u 2,t f 2,t [ ] A 1 A 2 F t = F t 1 + Bu t (5) I Quarterly and monthly variables So that the modeling described here can be used with monthly and quarterly frequencies, we use the transformation proposed in Mariano & Murasawa (2003). This allows quarterly variables such as GDP to be explained by other variables with monthly frequencies (factors) if we obtain quarterly representations for the monthly variable. Let Yt M be the level of a monthly variable representing the flow. The quarterly representation of this variable Y Q t is given by: Y Q t = Y M t + Y M t 1 + Y M t 2, t = 3, 6, 9,... (6) We define y t = Yt M follows: Y M t 1 and y Q t = Y Q t Y Q t 3. Hence we can write yq t as a function of y t as y Q t = y t + 2y t 1 + 3y t 2 + 2y t 3 + y t 4, t = 3, 6, 9,... (7) Monthly differences can thus be transformed into quarterly differences. If the variable of interest is a rate of change, the following filter is a possible approximation: log(y Q t ) log(y t ) + 2log(y t 1 ) + 3log(y t 2 ) + 2log(y t 3 ) + log(y t 4 ) (8) 3

4 2.3 Estimation The methodology for estimating dynamic factor will be Two Stages, which the explanatory variables in x t are assumed to have the same frequency (monthly), while the response variable is assumed to be quarterly. This approach is described in the seminal work by Giannone et al. (2008). In the case of the exact DFM, the dynamic factors are estimated in two steps. In the first stage, the parameters of the matrices Λ and f t are estimated by Principal Components Analysis (PCA) using a standardized, balanced panel ( X t ), in which there is no missing values and outliers. Standardization is important as PCA is not scale invariant. The estimators ˆΛ and ˆf t can be obtained by solving the following optimization problem: 1 min f 1,...,f T,Λ NT T ( X t Λf t ) ( X t Λf t ) s.t. N 1 Λ Λ = I r (9) t=1 The estimator for the variance and covariance matrix for ε t is then given by ( 1 ˆΨ = diag T ) T ( X t ˆΛ ˆf t )( X t ˆΛ ˆf t ) t=1 According to Stock & Watson (2011), the solution to (9) is to set ˆΛ equal to the eigenvectors of the variance and covariance matrix of Xt associated with the r largest eigenvalues, from which it follows that ˆf t is the r first principal components of Xt. The coefficients of the matrix A i, i = 1, 2,..., p, are estimated by OLS regression of f t on f t 1,..., f t p. Finally, BB is estimated as the covariance matrix of the residuals of this regression. In the second stage, Kalman smoothing (Durbin & Koopman, 2012) is used to re-estimate the factors for the unbalanced panel x t considering the parameters obtained in the previous step. Two options can be considered when estimating the factors: quarterly factors: the monthly explanatory variables can be transformed to represent quarterly quantities using the procedure outlined in section 2.2. Thus, although the factors are monthly, they will also represent quarterly quantities and can be transformed into variables with a quarterly frequency, allowing equation (11) to be estimated and the response variable to be forecast. monthly factors: another option is to estimate the factors using the original variables and then apply the transformation described in section 2.2 to the factors so that they represent quarterly quantities. The quarterly variable that will be used to forecast the response variable in (11) is then created. (10) y t = β 0 + β ˆft + e t (11) The parameters of equation (11) are estimated by OLS, and the forecast for y t+h is given by ŷ t+h = ˆβ 0 + ˆβ ˆft+h (12) 4

5 2.4 Database The response variable that we wish to forecast is the first vintage of the Brazilian GDP, i.e., the first GDP released for each quarter without any revisions. This variable is released quarterly by the IBGE (Official Brazilian Bureau of Statistics) and was collected without any seasonal adjustment. The transformation to make it stationary is the monthly difference in the year-over-year rate of change. Although the forecast variable is the level, analysts are interested in forecasting the variable as a quarter-over-quarter rate of change (i.e., comparison with the previous quarter) and a year-over-year rate of change (i.e., comparison with the same quarter of the previous year). To determine the quarterover-quarter rate of change the seasonality of the variable in levels must first be removed. This adjustment was performed with X-13ARIMA-SEATS software according to the IBGE methodology 2. The explanatory variables used to forecast Brazilian GDP were selected based on the opinions of specialists with experience of forecasting Brazilian GDP and the papers by Giannone et al. (2008) and Banbura et al. (2011). In all, 128 variables were used. These are shown in Table 1 (Appendix), which also shows which transformation was used with each variable to make it stationary. The transformations were: 0 - the observed series is preserved; 1 - monthly rate of change; 2: monthly difference; 3: monthly difference in year-over-year rate of change; 4: monthly difference in year-over-year difference. The table also shows the time in days before the variable is released (delay). This last piece of information is useful for estimating forecasts for a period in the past and allows a database supposedly observed on a specific date to be created. 3 Forecasting, nowcasting and backcasting Brazilian GDP The following results show the performance of the final model using the dynamic factor methodology described in section 2. To define the model and achieve a satisfactory result with a low forecast error, we used different combinations of the number of dynamic factors r, the lag order p of the factors, the number of shocks q in the factors and the two options for estimating the factors. The chosen combination was p = 2, q = 2 and r = 2 with the monthly factors estimation method using all the variables in Table 1 in the Appendix. To assess the performance of the model, forecasts were made every Friday from November 7, 2014, to December 31, 2017, for the first, second, third and fourth quarters of 2015, 2016 and Figure 1 shows the GDP forecasts (quarter-over-quarter and year-over-year variations) for these quarters. The blue bars in the graph correspond to the 1st GDP vintage released by the IBGE. The red lines represent all the variations forecast for a particular quarter. The red dot indicates the last forecast for the GDP using the model defined in the previous paragraph (backcasting). The graphs show that there is a quite large variation in the forecasts and that these are not concentrated around the red dot. However, the last forecast (the red dot) is usually a good estimate. The graphs also show that the forecast error for the year-over-year variation in some quarters (e.g., the last two quarters of 2017) is generally less than the forecast error for the quarterly variation. In the case of the year-over-year variation, the forecast and observed values are very close, while in the case of the quarterly variation, 2 ftp://ftp.ibge.gov.br/contas_nacionais/contas_nacionais_trimestrais/ajuste_sazonal/x13_ NasContasTrimestrais.pdf 5

6 Quarterly growth (quarter over quarter) Year growth (year over year) Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q4 Figure 1: Forecast of Brazilian GDP from 2015-Q1 to 2017-Q Quarterly growth (quarter over quarter) Forecasting Nowcasting Backcasting Year growth (year over year) Forecasting Nowcasting Backcasting Figure 2: RMSE of forecasts of Brazilian GDP during 28 weeks prior to its release they are some distance apart, perhaps because of the seasonal adjustment. This may indicate the need for some adjustments to be made. Figure 2 shows the RMSE (Root Mean Square Error) for 28 weeks of forecasts before GDP is released. The first eight weeks are classified as forecasting, as the forecasts were made before the beginning of the quarter in question. The following twelve weeks (1 quarter) correspond to nowcasting, and the last eight to backcasting. As the week in which the GDP is released approaches, the RMSE decreases. The largest errors occur during the forecasting phase, when there is not yet any information in the database about the quarter in question. For both the quarterly variation, for which a seasonal adjustment is required, and the year-over-year variation there is an error of approximately 0.5 in the last weeks before the GDP is released. Figure 3 shows the weekly estimate of the year-over-year variation in GDP for The dotted red line corresponds to the GDP released by the IBGE. Note that the forecast error is greater in the two months preceding the reference quarter. As the variables are released each week, the error begins to decrease, as mentioned in the previous paragraph. The best forecasts are those made less than one 6

7 month before the GDP is released. This figure also shows the Focus Bulletin forecast 3 (the median of specialists forecasts). The forecast error for the specialists forecasts is less than the error for the model used in this paper during the forecasting and nowcasting phases. In the backcasting phase, however, the errors are similar. Seasonally adjusted quarterly GDP forecasts are not included in the Focus Bulletin and are therefore not shown here Q1 Year Growth (year over year) 2017 Q2 Year Growth (year over year) model Focus observed GDP model Focus observed GDP Q3 Year Growth (year over year) Q4 Year Growth (year over year) model Focus observed GDP model Focus observed GDP Figure 3: Forecast based on DFM vs. Focus Bulletin forecast 4 Final Considerations The results in section 3 indicate that dynamic factor models (section 2.1) can provide good results when used to estimate Brazilian GDP. In this paper we used only two factors to summarize the information contained in 128 monthly variables and forecast quarterly GDP. Forecast errors were analyzed for 28 weeks prior to the release of the GDP for the reference period. As expected, forecast errors decreased significantly as the release date approached, particularly during the last four weeks, when the smallest historical error (RMSE = 0.5) was observed. There was, however, significant dispersion of the forecasts, the largest errors being observed during the forecasting phase and the beginning of the nowcasting phase. Another important finding concerns the forecast of the quarterly variation, which requires seasonal adjustment of the GDP. Comparison of the forecast error for the year-over-year variation, which does not require a seasonal adjustment, and the error for the quarterly variation reveals that the latter is greater even when the forecast year-over-year variation is very close to the observed value. To assess the quality of the forecast, we compared the forecasts based on the dynamic factor 3 The Focus Bulletin gives a forecast of Brazilian GDP based on estimates by the best specialists. 7

8 methodology with Focus Bulletin forecasts, which are based on the median of specialists estimates of Brazilian GDP. The Focus Bulletin forecast has the lower error during the forecasting and nowcasting phases. In the four weeks immediately prior to the release of the GDP, both forecasts are closer. In light of these findings and to further reduce the forecast error, we intend in future to investigate whether all the variables actually contribute to the GDP forecast and thus determine whether forecast accuracy could be improved by removing some variables. We also intend to use the Expectation- Maximization methodology described in Banbura et al. (2011) after carefully defining the main variables that help to forecast GDP, as this methodology requires greater computational capacity. References Aastveit, K. A., & Trovik, T. (2012). Nowcasting norwegian gdp: the role of asset prices in a small open economy. Empirical Economics, 42 (1), URL Angelini, E., Camba-Mendez, G., Giannone, D., Reichlin, L., & Rünstler, G. (2008). Short-Term Forecasts of Euro Area GDP Growth. Working Papers ECARES ECARES , ULB Universite Libre de Bruxelles. URL Banbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting. Oxford Handbook on Economic Forecasting. Bańbura, M., & Rünstler, G. (2011). A look into the factor model black box: publication lags and the role of hard and soft data in forecasting gdp. International Journal of Forecasting, 27 (2), Boivin, J., & Ng, S. (2006). Are more data always better for factor analysis? Journal of Econometrics, 132 (1), Chauvet, M. (2001). A monthly indicator of brazilian gdp. Brazilian Review of Econometrics, 21 (1), URL D Agostino, A., Domenico, G., & Surico, P. (2006). (Un)Predictability and Macroeconomic Stability. Research Technical Papers 5/RT/06, Central Bank of Ireland. URL D Agostino, A., McQuinn, K., & O Brien, D. (2008). Now-casting irish gdp. Research Technical Papers 9/RT/08, Central Bank of Ireland. URL Dahlhaus, T., Guenette, J.-D., & Vasishtha, G. (2015). Nowcasting bric+m in real time. Staff working papers, Bank of Canada. URL 8

9 Durbin, J., & Koopman, S. J. (2012). Time Series Analysis by State Space Methods. Oxford University Press, 2 ed. URL Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2004). The generalized dynamic factor model consistency and rates. Journal of Econometrics, 119 (2), Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55 (4), Marcellino, M., Stock, J., & Watson, M. (2003). Macroeconomic forecasting in the euro area: Country specific versus area-wide information. European Economic Review, 47 (1), URL Mariano, R. S., & Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. Journal of applied Econometrics, 18 (4), Matheson, T. D. (2010). An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys. Economic Modelling, 27 (1), URL Mattos, D. M., Gomes, G. B., & Ferreira, P. C. (2018). nowcasting: Nowcast Analysis and Create Real-Time Data Basis. R package version URL Stock, J., & Watson, M. (2016). Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics. vol. 2, chap. Chapter 8, (pp ). Elsevier. URL Stock, J. H., & Watson, M. (2011). Dynamic factor models. Oxford Handbook on Economic Forecasting. Stock, J. H., & Watson, M. W. (2006). Forecasting with many predictors. Handbook of economic forecasting, 1, Van Nieuwenhuyze, C., Ruth, K., Rua, A., Jelonek, P., Jakaitiene, A., Den Reijer, A., Cristadoro, R., Rünstler, G., Benk, S., & Barhoumi, K. (2008). Short-term forecasting of gdp using large monthly datasets: a pseudo real-time forecast evaluation exercise. Occasional Paper Series 84, European Central Bank. URL 9

10 Appendix Table 1: Explanatory variables 10 Series code Series name Transformation Delay 1 serie1 Exchange rate - Free - United States dollar (sale) - 1 u.m.c/us$ serie12 Interest rate - CDI serie188 National Consumer Price Index (INPC) serie189 General Price Index-Market (IGP-M) serie192 National Index of Building Costs (INCC) serie193 Consumer Price Index - Săo Paulo (IPC-FIPE) serie194 Cost of Living Index (ICV-Dieese) serie206 Staple food basket u.m.c serie225 Wholesale Price Index (IPA) serie432 Interest rate - Selic target serie433 Broad National Consumer Price Index (IPCA) serie1178 Interest rate - Selic in annual terms (basis 252) serie1344 Installed capacity utilization- General(FGV) serie1373 Vehicle production (total) serie1374 Passenger cars and light commercial vehicles production serie1375 Truck production serie1376 Bus production serie1377 Motorcycle production serie1378 Vehicle sales (total) serie1379 Domestic vehicle sales serie1380 Vehicle exports serie1382 Productions of mechanized cultivators serie1383 Four wheel tractor production serie1384 Track driven tractor production serie1385 Production of combined harvesters serie1386 Production of mechanical diggers serie1387 Other agricultural machinery 3 14

11 11 Table 1 continued from the previous page Series code Series name Transformation Delay 28 serie1388 Production of agricultural machinery (total) serie1389 Petroleum derivatives production - crude oil serie1390 Petroleum derivatives production - LGN serie1391 Petroleum derivatives production - total serie1392 Petroleum derivatives production - natural gas serie1402 Electric energy consumption - Brazil - commercial serie1403 Electric energy consumption - Brazil - residential serie1404 Eletric energy consumption - Brazil - industrial serie1405 Eletric energy consumption - Brazil - other serie1406 Eletric energy consumption - Brazil - total serie1453 Credit sales Index serie1454 Cash Sales Index serie1455 Sales volume index in the retail sector - Total- Brazil serie1483 Sales volume index in the retail sector - Fuel and lubricants- Brazil serie1496 Sales volume index in the retail sector - Hypermarkets, supermarket, food, beverages and tobacco- Brazil serie1522 Sales volume index in the retail sector - Furniture and white goods- Brazil serie1509 Sales volume index in the retail sector - Textiles, Clothing and Footwear- Brazil serie1548 Sales volume index in the retail sector - Vehicles and motorcycles, spare parts- Brazil serie1561 Sales volume index in the retail sector - Hypermarkets, supermarket- Brazil serie2053 Net public debt - Balances in c.m.u. (million) - Total- Federal Government and Brazilian Central Bank serie2057 Net public debt - Balances in c.m.u. (million) - Total- State governments serie2058 Net public debt - Balances in c.m.u. (million) - Total- Municipal governments serie4393 Consumer confidence index serie4394 Current economic conditions index serie4395 Future expectations index serie4503 Net public debt (%GDP) - total- Federal Government and Brazilian Central Bank serie4506 Net public debt (% GDP) - Total- State and municipal governments serie4507 Net public debt (%GDP) - total- State governments serie4508 Net public debt (% GDP) - total- Municipal governments serie4509 Net public debt (%GDP) - total- State enterprises 2 35

12 12 Table 1 continued from the previous page Series code Series name Transformation Delay 58 serie4513 Net public debt(%gdp) - total- consolidated public sector serie7341 Business confidence index- General serie7357 Steel production (1992= 100) serie7384 Sales of factory authorized vehicle outlets - Passenger cars sales serie7385 Sales of factory authorized vehicle outlets - Light commercial cars sales serie7386 Sales of factory authorized vehicle outlets - Truck sales serie7412 Balanced checks - in serie7413 Returned checks - in serie7415 BNDES system disbursements - Total serie7416 BNDES system disbursements - Manufacturing industry serie7417 BNDES system disbursements - Commerce and Services serie7418 BNDES system disbursements - Agricultural sector serie7419 BNDES system disbursements - Vegetable extraction serie7478 National consumer Price Index- Extended 15 (IPCA-15) serie7495 SINAPI serie7832 Ibovespa- monthly percent change serie13667 Percent of 90 days past due loans of financial institutions under public control - Total serie13673 Percent of 90 days past due loans of financial institutions under national private control - Total serie13679 Percent of 90 days past due loans of financial institutions under foreign control - Total serie13685 Percent of 90 days past due loans of financial institutions under private control - Total serie20048 Commodity Index - Brazil (until Nov/2017) serie20050 Commodity Index - Brazil Agriculture (until Nov/2017) serie20051 Commodity Index - Brazil metal (until Nov/2017) serie20052 Commodity Index - Brazil Energy (until Nov/2017) serie20099 Pharmac., medical, orthop, and perfumery articles serie20101 Books, newspaper, megazines serie20102 Office, comp./ comunic, equip serie20104 Other art. Of personal use serie20105 Building materials serie20106 Broad trade sector 3 70

13 13 Table 1 continued from the previous page Series code Series name Transformation Delay 88 serie20341 Residential Real Estate Collateral Value Index serie21637 PMS- Nominal revenue from services - Total - Brazil serie21859 General (2012=100) serie21861 Physical Production - Mineral extraction serie21862 Physical Production - Manufacturing Industry serie21863 Physical Production - Capital goods serie21864 Physical Production - Intermediate goods serie21865 Physical Production - Consumer goods serie21866 Physical Production - Durable goods serie21867 Physical Production - Semi durable and non durable goods serie21868 Physical Production - Production of construction inputs serie21924 Industrial Production (2012=100) - Southeast Region serie21930 Industrial Production (2012=100) - Northern Region serie21934 Industrial Production (2012=100) - South serie21939 Industrial Production (2012=100) - Central- Western Region serie21961 Industrial Production (2012=100) - Northeast serie22704 Balance on goods and services - monthly - net serie22705 Balance on goods and services - monthly - credit serie22706 Balance on goods and services - monthly - debit serie22707 Balance on goods- Balance of Payments - monthly- net serie24352 Capacity utilization- manufacturing industry (FGV) serie24369 Unemployment - PNAD serie24370 Working age population - Continuous PNAD serie24378 Labor force population - Continuous PNAD serie24379 Employed population - Continuous PNAD serie24382 Real habitually average earnings of employed people- Continuous PNAD serie25241 Registered employees Index - Manufacturing ICI ICI with seasonal adjust - Industry confidence index ISA ISA with seasonal adjust - Industry confidence index - Actual situation IE IE with seasonal adjust - Industry confidence index - Expectation 1 0

14 Table 1 continued from the previous page Series code Series name Transformation Delay 118 ICOM ICOM with seasonal adjust-retail sector extended index ISACOM ISA-COM with seasonal adjust - Retail sector extended actual situation index IECOM IE-COM with seasonal adjust - Retail sector extended expected index ICS ICS with seasonal adjust - Service confidence index ISAS ISA-S with seasonal adjust - Service confidence index - Actual situation IES IE-S with seasonal adjust - Service confidence index - Expectation IAEAGRO Economy agriculture activities IAEIND Economy industrial activities IAESERV Economy services activities IAEIMP Economy taxes activities IAE Economy activities