Hostname: page-component-cb9f654ff-lqqdg Total loading time: 0 Render date: 2025-08-28T01:46:16.792Z Has data issue: false hasContentIssue false
  • English
  • Français

Do Economic Restrictions Improve Forecasts?

Published online by Cambridge University Press:  28 April 2015

Elizabeth Murphy ,
Bailey Norwood  and
Michael Wohlgenant
Elizabeth Murphy
Affiliation:
Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC
Bailey Norwood
Affiliation:
Department of Agricultural Economics, Oklahoma State University, Stillwater, OK
Michael Wohlgenant
Affiliation:
Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC
Get access Rights & Permissions [Opens in a new window]

Abstract

A previous study showed that imposing economic restrictions improves the forecasting ability of food demand systems, thus warranting their use even when they are rejected in-sample. This article evaluates whether this result is due to economic restrictions enhancing degrees of freedom or containing nonsample information. Results indicate that restrictions improve forecasting ability even when they are not derived from economic theory, but theoretical restrictions forecast best.

Keywords

demand systemseconomic restrictionsforecastingrepresentative consumerB4C1C3C5

Information

Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 36 , Issue 3 , December 2004 , pp. 549 - 558
Copyright
Copyright © Southern Agricultural Economics Association 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Ashley, R., Granger, C.W.J, and Schmalensee, R.. “Advertising and Aggregate Consumption: An Analysis of Causality.” Econometrica 48(1980):1149–67.CrossRefGoogle Scholar
Brieman, L. “The Little Bootstrap and Other Methods for Dimensionality Selection in Regression: X-Fixed Prediction Error.” Journal of the American Statistical Association 87(1992):738–54.CrossRefGoogle Scholar
Deaton, A., and Muellbauer, J.. “An Almost Ideal Demand System.” American Economic Review 70(1980):312–26.Google Scholar
Kastens, T.L., and Brester, G.W.. “Model Selection and Forecasting Ability of Theory-Constrained Food Demand Systems.” American Journal of Agricultural Economics 78,2(1996):301–12.CrossRefGoogle Scholar
Mendenhall, W, Wackerly, D.D., and Schaef-fer, R.L.. Mathematical Statistics With Applications, 4th ed. Boston: PWS-Kent Publishing Company, 1990.Google Scholar
Sawa, T. “Information Criteria For Discriminating Among Alternative Regression Models.” Econometrica 46,6(1978):1273–91.CrossRefGoogle Scholar