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Comparative Performance of U.S. Econometric Models$
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Lawrence R. Klein

Print publication date: 1991

Print ISBN-13: 9780195057720

Published to Oxford Scholarship Online: October 2011

DOI: 10.1093/acprof:oso/9780195057720.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 27 October 2021

New Methods for Using Monthly Data to Improve Forecast Accuracy

New Methods for Using Monthly Data to Improve Forecast Accuracy

(p.227) Chapter 8 New Methods for Using Monthly Data to Improve Forecast Accuracy
Comparative Performance of U.S. Econometric Models

E. Philip Howrey

Oxford University Press

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

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