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

  • Ericsson, Neil R.

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.

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Article provided by Elsevier in its journal Journal of Policy Modeling.

Volume (Year): 14 (1992)
Issue (Month): 4 (August)
Pages: 465-495

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Handle: RePEc:eee:jpolmo:v:14:y:1992:i:4:p:465-495
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/505735

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