Unit Root Tests are Useful for Selecting Forecasting Models

Francis Diebold and Lutz Kilian

New York University, Leonard N. Stern School Finance Department Working Paper Seires from New York University, Leonard N. Stern School of Business-

Abstract: We study the usefulness of unit root tests as diagnostic tools for selecting forecasting models. Difference stationary and trend stationary models of economic and financial time series often imply very different predictions, so deciding which model to use is tremendously important for applied forecasters. We consider three strategies: always difference the data, never difference, or use a unit-root pretest. We characterize the predictive loss of these strategies for the canonical AR(1) process with trend, focusing on the effects of sample size, forecast horizon, and degree of persistence. We show that pretesting routinely improves forecast accuracy relative to forecasts from models in differences, and we give conditions under which pretesting is likely to improve forecast accuracy relative to forecasts from models in levels.

Date: 1999-01-18
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Related works:
Journal Article: Unit-Root Tests Are Useful for Selecting Forecasting Models (2000)
Working Paper: Unit Root Tests Are Useful for Selecting Forecasting Models (1999)
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