Applying a microfounded-forecasting approach to predict Brazilian inflation
Wagner Gaglianone (),
João Issler () and
Silvia Maria Matos ()
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Silvia Maria Matos: Getulio Vargas Foundation
Empirical Economics, 2017, vol. 53, issue 1, No 9, 137-163
Abstract We investigate whether combining forecasts from surveys of expectations is a helpful empirical strategy for forecasting inflation in Brazil. We employ the FGV–IBRE Economic Tendency Survey, which consists of monthly qualitative information from approximately 2000 consumers since 2006, and also the Focus Survey of the Central Bank of Brazil, with daily forecasts since 1999 from roughly 250 professional forecasters. Natural candidates to win a forecast competition in the literature of surveys of expectations are the (consensus) cross-sectional average forecasts (AF). We first show that these forecasts are a bias-ridden version of the conditional expectation of inflation using the no-bias tests proposed in Issler and Lima (J Econom 152(2):153–164, 2009) and Gaglianone and Issler (Microfounded forecasting, 2015). The results reveal interesting data features: Consumers systematically overestimate inflation (by 2.01 p.p., on average), whereas market agents underestimate 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 (J Forecast 3:197–204, 1984) forecast combination (GR) technique, a Phillips-curve based method, the Capistrán and Timmermann (J Bus Econ Stat 27:428–440, 2009) combination method, 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. The Phillips-curve based method has a median performance in terms of MSE, and the Capistrán and Timmermann (2009) combination method fares slightly worse.
Keywords: Consensus forecasts; Forecast combination; Common features; Panel data (search for similar items in EconPapers)
JEL-codes: C14 C33 E37 (search for similar items in EconPapers)
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Working Paper: Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation (2016)
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