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More Realistic Single Equation Models Through Specification of Random Coefficients*

Published online by Cambridge University Press:  28 April 2015

Max R. Langham  and
Michael Mara
Max R. Langham
Affiliation:
University of Florida
Michael Mara
Affiliation:
University of Florida
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Extract

Regression analysis with its many modifications and extensions plays a dominant role as an analytical tool in economic research. The linear regression model with random coefficients (hereafter RCR for random coefficient regression) provides a generalization of the classical linear regression model and permits a more realistic specification of the real world than does the classical model. As a consequence RCR will probably play an increasingly important role in econometric analysis of a wide class of problems-particularly as probabilistic micro-economic theory develops.

Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 5 , Issue 1 , July 1973 , pp. 161 - 166
Copyright
Copyright © Southern Agricultural Economics Association 1973

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Footnotes

*

Florida Agricultural Experiment Station Journal Series No. 4891 under State Project AS 01636.

References

[1]Froehlich, B. R., “Estimation of a Random Coefficient Regression Model,” unpublished PhD. Thesis, University of Minnesota, 1971.Google Scholar
[2]Hildreth, Clifford and Houck, J. P., “Some Estimators for a Linear Model with Random Coefficients,” Journal of the American Statistical Association, 63, 322, June, 1968, 584–595.Google Scholar
[3]Hurwicz, Leonid, “Systems with Nonadditive Disturbances,” Statistical Inference in Dynamic Economic Models, edited by Koopmans, T. C.. Cowles Commission Monograph No. 10, 1950, 410–418.Google Scholar
[4]Rubin, Herman, “Note on Random Coefficients,” Statistical Inference in Dynamic Economic Models, edited by Koopmans, T. C.. Cowles Commission Monograph No. 10, 1950, 419–421.Google Scholar
[5]Singh, Balvir, Nagar, A. L. and Raj, Baldev, “Measurement of Structural Change (An Application of Random Coefficient Regression Model),” unpublished paper, March, 1972.Google Scholar
[6]Swamy, P. A. V. D., “Efficient Inference in a Random Coefficient Regression Model,” Econometrica, 38, 2 March, 1970, 311–323.CrossRefGoogle Scholar
[7]Swamy, P. A. V. D., “Statistical Inference in Random Coefficient Regression Models,” unpublished PhD. Thesis, University of Wisconsin, 1968.Google Scholar
[8]Theil, Henry, Principles of Econometrics. New York: John Wiley and Sons, Inc., 1971.Google Scholar
[9]Theil, H. and Mennes, L. B. M., “Multiplicative Randomness in Time Series Regression Analysis,” Mimeographed Report No. 5901, Econometric Institute of the Netherlands School of Economics, 1959.Google Scholar
[10]Zellner, Arnold, “On the Aggregation Problem: A New Approach to a Troublesome Problem,” Report No. 6628, Center for Mathematical Studies in Business and Economics, University of Chicago, 1966, earlier draft July 25, 1965.Google Scholar