FORECASTING US INFLATION USING DYNAMIC GENERAL-TO-SPECIFIC MODEL SELECTION

George Bagdatoglou, Alexandros Kontonikas () and Mark Wohar ()

Bulletin of Economic Research, 2016, vol. 68, issue 2, 151-167

Abstract: type="main">

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out-of-sample forecasts are generated from models identified from a multipath general-to-specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well-established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.

Date: 2016
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