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Detecting and predicting forecast breakdowns

Author

Listed:
  • Giacomini, Raffaella
  • Rossi, Barbara

Abstract

We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss function, is significantly worse than its in-sample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data generating process and relate the properties of our forecast breakdown test to those of existing structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips' curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed. JEL Classification: C22, C52, C53

Suggested Citation

  • Giacomini, Raffaella & Rossi, Barbara, 2006. "Detecting and predicting forecast breakdowns," Working Paper Series 638, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:2006638
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    References listed on IDEAS

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    More about this item

    Keywords

    forecast evaluation; Forecast rationality testing; In-sample evaluation; Out-of-sample evaluation; structural change;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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