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Forecasting Macroeconomic Variables under Model Instability

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  • Pettenuzzo, Davide
  • Timmermann, Allan G

Abstract

We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. GDP growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model which is a specification that allows for time-varying parameters and stochastic volatility.

Suggested Citation

  • Pettenuzzo, Davide & Timmermann, Allan G, 2016. "Forecasting Macroeconomic Variables under Model Instability," CEPR Discussion Papers 11355, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:11355
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    References listed on IDEAS

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    Cited by:

    1. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    2. Gary Koop & Dimitris Korobilis, 2015. "Forecasting With High Dimensional Panel VARs," Working Papers 2015_25, Business School - Economics, University of Glasgow.
    3. Korobilis, Dimitris & Koop, Gary, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Essex Finance Centre Working Papers 22665, University of Essex, Essex Business School.
    4. repec:bla:obuest:v:81:y:2019:i:5:p:937-959 is not listed on IDEAS
    5. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.

    More about this item

    Keywords

    GDP growth; inflation; regime switching; stochastic volatility; time-varying parameters;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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