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Advances in Forecasting under Instability

In: Handbook of Economic Forecasting

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  • Rossi, Barbara
Abstract
The forecasting literature has identified two important issues: (i) several predictors have substantial and statistically significant predictive content, although only sporadically, and it is unclear whether this predictive content can be exploited reliably; (ii) in-sample predictive content does not necessarily translate into out-of-sample predictive ability, nor ensures the stability of the predictive relationship over time. The objective of this chapter is to understand what we have learned about forecasting in the presence of instabilities. The empirical evidence raises a multitude of questions. If in-sample tests provide poor guidance to out-of-sample forecasting ability, what should researchers do? If there are statistically significant instabilities in Granger-causality relationships, how do researchers establish whether there is any Granger-causality at all? If there is substantial instability in predictive relationships, how do researchers establish which model is the “best†forecasting model? And finally, if a model forecasts poorly, why is that and how should researchers proceed to improve the forecasting models? In this chapter, we answer these questions by discussing various methodologies for inference as well as estimation that have been recently proposed in the literature.

Suggested Citation

  • Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    • Handle: RePEc:eee:ecofch:2-1203
    DOI: 10.1016/B978-0-444-62731-5.00021-X
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    More about this item

    Keywords

    Forecasting; Instabilities; Structural breaks; Time-varying parameters; In-sample fit;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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