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Forecasting Us Inflation Using Dynamic General-To-Specific Model Selection

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Listed:
  • George Bagdatoglou
  • Alexandros Kontonikas
  • Mark E. Wohar

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.

Suggested Citation

  • George Bagdatoglou & Alexandros Kontonikas & Mark E. Wohar, 2016. "Forecasting Us Inflation Using Dynamic General-To-Specific Model Selection," Bulletin of Economic Research, Wiley Blackwell, vol. 68(2), pages 151-167, April.
  • Handle: RePEc:bla:buecrs:v:68:y:2016:i:2:p:151-167
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    References listed on IDEAS

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