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More Realistic Single Equation Models Through Specification of Random Coefficients

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  • Langham, Max R.
  • Mara, Michael

Abstract

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.

Suggested Citation

  • Langham, Max R. & Mara, Michael, 1973. "More Realistic Single Equation Models Through Specification of Random Coefficients," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 5(1), pages 161-166, July.
    • Handle: RePEc:cup:jagaec:v:5:y:1973:i:01:p:161-166_01
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    1. Epplin, Francis M. & Berte, Kama & Krenzer, Eugene G., Jr. & Heer, William F., 1988. "Influence of Alternative Planting Dates and Tillage Systems on Winter Wheat Production Response: A Random Coefficients Approach," 1988 Annual Meeting, August 1-3, Knoxville, Tennessee 270307, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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