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Forecasting Inflation Using Dynamic Model Averaging

Author

Listed:
  • Gary Koop

    (Department of Economics, University of Strathclyde and RCEA)

  • Dimitris Korobilis

    (Department of Economics, University of Strathclyde and RCEA)

Abstract
There is a large literature on forecasting inflation using the generalized Phillips curve (i.e. using forecasting models where inflation depends on past inflation, the unemployment rate and other predictors). The present paper extends this literature through the use of econometric methods which incorporate dynamic model averaging. These not only allow for coefficients to change over time (i.e. the marginal effect of a predictor for inflation can change), but also allows for the entire forecasting model to change over time (i.e. different sets of predictors can be relevant at different points in time). In an empirical exercise involving quarterly US inflation, we find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark approaches (e.g. random walk or recursive OLS forecasts) and more sophisticated approaches such as those using time varying coefficient models.

Suggested Citation

  • Gary Koop & Dimitris Korobilis, 2009. "Forecasting Inflation Using Dynamic Model Averaging," Working Paper series 34_09, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:34_09
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Option Pricing; Modular Neural Networks; Non-parametric Methods;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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