New Methods for Using Monthly Data to Improve Forecast Accuracy

E. Philip Howrey

in Comparative Performance of U.S. Econometric Models

Published in print June 1991 | ISBN: 9780195057720
Published online October 2011 | e-ISBN: 9780199854967 | DOI:
New Methods for Using Monthly Data to Improve Forecast Accuracy

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Econometric forecasters continually seek ways to increase forecast accuracy. As new data are released, the residuals of forecasting models are examined for evidence of structural change and equations are modified if necessary. Several of the participants in the Model Comparison Seminar have recently investigated alternative methods for using monthly data in a systematic way to adjust forecasts produced by quarterly models. These initial studies are reviewed in this chapter and some illustrative results are presented. It begins with a review of some of the implications of temporal aggregation for the specification and estimation of models and their use in economic forecasting. This review is intended to provide motivation for the use of high-frequency (monthly) data in forecasting economic aggregates, as well as to indicate some of the difficulties that are involved. The chapter concludes with a presentation of some illustrative results obtained using the Michigan Quarterly Econometric Model of the United States economy.

Keywords: economic forecasting; forecast accuracy; structural change; temporal aggregation; econometric models; high-frequency data; economic aggregates; United States; economy

Chapter.  8538 words.  Illustrated.

Subjects: Econometrics and Mathematical Economics

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