Parameter constancy, mean square forecast errors, and measuring forecast performance: an exposition, extensions, and illustration

Neil R. Ericsson ()

No 412, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Parameter constancy and a model's mean square forecast error are two commonly used measures of forecast performance. By explicit consideration of the information sets involved, this paper clarifies the roles that each plays in analyzing a model's forecast accuracy. Both criteria are necessary for "good" forecast performance, but neither (nor both) is sufficient. Further, these criteria fit into a general taxonomy of model evaluation statistics, and the information set corresponding to a model's mean square forecast error leads to a new test statistic, forecast-model encompassing. Two models of U.K. money demand illustrate the various measures of forecast accuracy.

Keywords: Forecasting (search for similar items in EconPapers)
Date: 1991
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Journal Article: Parameter constancy, mean square forecast errors, and measuring forecast performance: An exposition, extensions, and illustration (1992)
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