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Cointegration And The Forecast Accuracy Of Var Models

Author

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  • Maria M. De Mello

    (CEF.UP, Faculdade de Economia, Universidade do Porto)

Abstract

This paper assesses the forecast performance of a set of VAR models under a growing number of restrictions. With a maximum forecast horizon of 12 years, we show that the farther the horizon is, the more structured and restricted VAR models have to be to produce accurate forecasts. Indeed, unrestricted VAR models, not subjected to integration or cointegration, are poor forecasters for both short and long run horizons. Differenced VAR models, subject to integration, are reliable predictors for one-step horizons but ineffectual for multi-step horizons. Cointegrated VAR models including appropriate structural breaks and exogenous variables, as well as being subjected to over-identifying theory consistent restrictions, are excellent forecasters for both short and long run horizons. Hence, to obtain precise forecasts from VAR models, proper specification and cointegration are crucial for whatever horizons are at stake, while integration is relevant only for short run horizons.

Suggested Citation

  • Maria M. De Mello, 2009. "Cointegration And The Forecast Accuracy Of Var Models," CEF.UP Working Papers 0902, Universidade do Porto, Faculdade de Economia do Porto.
    • Handle: RePEc:por:cetedp:0902
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    Cited by:

    1. Moosa, Imad A. & Vaz, John J., 2016. "Cointegration, error correction and exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 21-34.
    2. Gerdesmeier Dieter & Roffia Barbara & Reimers Hans-Eggert, 2017. "Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 19-34, December.

    More about this item

    Keywords

    VAR demand systems; structural breaks; exogenous regressors; integration; cointegration; forecast accuracy.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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