Unit Root Tests are Useful for Selecting Forecasting Models
Francis X. Diebold
University of Pennsylvania - Department of Economics; National Bureau of Economic Research (NBER)
University of Michigan at Ann Arbor - Department of Economics; Centre for Economic Policy Research (CEPR)
NBER Working Paper No. w6928
We study the usefulness of 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. Forecasters face three choices: 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.
Number of Pages in PDF File: 30working papers series
Date posted: March 26, 1999
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