Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation

In this paper, we investigate whether combining forecasts from surveys of expectations is a helpful strategy for forecasting inflation in Brazil. We employ the FGV-IBRE Economic Tendency Survey, which consists of monthly qualitative information from approximately 2,000 consumers since 2006, and the Focus Survey of the Central Bank of Brazil, with daily forecasts since 1999 from roughly 250 registered professional forecasters. Natural candidates to win a forecast competition in the literature of surveys of expectations are the (consensus) cross-sectional average forecasts (AF). In an exploratory investigation, we first show that these forecasts are a bias ridden version of the conditional expectation of inflation. The no-bias tests are conducted for the intercept and slope using the methods in Issler and Lima (2009) and Gaglianone and Issler (2015). The results reveal interesting data features: consumers systematically overpredict inflation (by 2.01 p.p., on average), whereas market agents underpredict it (by -0.68 p.p. over the same sample). Next, we employ a pseudo out-of-sample analysis to evaluate different forecasting methods: the AR(1) model, the Granger and Ramanathan (1984) forecast combination (GR), the consensus forecast (AF), the Bias-Corrected Average Forecast (BCAF), and the extended BCAF. Results reveal that: (i) the MSE of the AR(1) model is higher compared to the GR (and usually lower compared to the AF); and (ii) the extended BCAF is more accurate than the BCAF, which, in turn, dominates the AF. This validates the view that the bias corrections are a useful device for forecasting using surveys.