Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-24T13:51:24.487Z Has data issue: false hasContentIssue false
  • English
  • Français

Examination of Alternative Heteroscedastic Error Structures Using Experimental Data

Published online by Cambridge University Press:  28 April 2015

James W. Mjelde ,
Oral Capps Jr.  and
Ronald C. Griffin
James W. Mjelde
Affiliation:
Department of Agricultural Economics, Texas A&M University
Oral Capps Jr.
Affiliation:
Department of Agricultural Economics, Texas A&M University
Ronald C. Griffin
Affiliation:
Department of Agricultural Economics, Texas A&M University
Get access Rights & Permissions [Opens in a new window]

Abstract

Impacts of alternative specifications for heteroscedastic error structures are examined by estimating various production functions for corn in Central Texas. Production- and profit-maximizing levels of inputs and the shape of the profit equation obtained from models not corrected for heteroscedasticity differed from those obtained from models corrected for heteroscedasticity. Using the profit-maximizing input levels for each production function gave essentially the same estimated yield and profit, regardless of the specification for heteroscedasticity employed. Differences of up to one-quarter to one-third are noted, however, in the amount of profit-maximizing levels of inputs used, depending on the heteroscedasticity correction.

Keywords

cornheteroscedasticityproduction functions
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 27 , Issue 1 , July 1995 , pp. 197 - 211
Copyright
Copyright © Southern Agricultural Economics Association 1995

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.)

References

Amemiya, T. “Regression Analysis When the Variance of the Dependent Variable is Proportional to the Square of its Expectation.” J. Amer. Stat. Association 68(1973):928–34.CrossRefGoogle Scholar
Amemiya, T. “A Note on a Heteroscedastic Model.” J. of Econometrics 6(1977):365–70.CrossRefGoogle Scholar
Battese, G.E. and Bonyhady, B.P.. “Estimation of Household Expenditure Functions: An Application of a Class of Heteroscedastic Regression Models.” The Economic Record 57(1981):80–5.CrossRefGoogle Scholar
Bay, T.F. and Schoney, R.A.. “Data Analysis with Computer Graphics: Production Functions.” Amer J. Agr. Econ. 64(1982):289–97.CrossRefGoogle Scholar
Breusch, T.S. and Pagan, A.R.. “A Simple Test for Heteroscedasticity and Random Coefficient Variation.” Econometrics 47(1979): 1287–94.CrossRefGoogle Scholar
Buccola, S.T. and McCarl, B.A.. “Small-Sample Evaluation of Mean-Variance Production Function Estimators.” Amer. J. of Ag. Econ. 68(1986):732–8.CrossRefGoogle Scholar
Carroll, R.J. and Ruppert, D.. “Transformation and Weighting in Regression.” New York. Chapman and Hall. 1988. pp. 249.CrossRefGoogle Scholar
Cothen, J.T. Personnel Communications. Professor, Soil and Crops Department. Texas A&M University. June 1989.Google Scholar
Debertin, D.L. “An Animated Instructional Module for Teaching Production Economics with 3-D Graphics.” Amer. J. Ag. Econ. 75(1993):485–491.CrossRefGoogle Scholar
Glejser, H. “A New Test for Heteroscedasticity.” J. Amer. Stat. Association 64(1969):316–23.CrossRefGoogle Scholar
Griffin, R.C., Montgomery, J.M., and Rister, M.E.. “Selecting Functional Form in Production Function Analysis.” W. J. Ag. Econ. 12(1987):216–227.Google Scholar
Griffin, R.C., Rister, M.E., Montgomery, J.M., and Turner, F.T.. “Scheduling Inputs with Production Functions: Optimal Nitrogen Programs for Rice.” S. J. Agr. Econ. 17(1985): 159–68.Google Scholar
Harvey, A.C. “Estimating Regression Models with Multiplicative Heteroscedasticity.” Econometrica 44(1976):461–5.CrossRefGoogle Scholar
Hildreth, C. and Houck, J.P.. “Some Estimators for a Linear Model with Random Coefficients.” J Amer. Stat. Association 63(1968):584–95.Google Scholar
Hildreth, C.G. “Discrete Models with Qualitative Descriptions.” Methodological Procedures in the Economic Analysis of Fertilizer Use Data, ed. Baum, E.L., Heady, E. O., and Blackmore, J., pp. 62–75. Ames: Iowa State College Press, 1955.Google Scholar
Judge, G.C, Hill, R.C., Griffiths, W.E., Lütkepohl, H., and Lee, T.C.. The Theory and Practice of Econometrics. First Edition. John Wiley and Sons, 1980.Google Scholar
Judge, G.C, Hill, R.C, Griffiths, W.E., Lütkepohl, H., and Lee, T.C.. Introduction to the Theory and Practice of Econometrics. John Wiley and Sons, 1982.Google Scholar
Judge, G.C, Griffiths, W.E., Hill, R.C., Lütkepohl, H., and Lee, T.C.. The Theory and Practice of Econometrics. Second Edition, John Wiley and Sons, 1985.Google Scholar
Just, R.E. and Pope, R.D.. “Stochastic Specification of Production Functions and Economic Implications.” J. Econometrics 7(1978):67–86.CrossRefGoogle Scholar
Just, R.E. and Pope, R.D.. “Production Function Estimation and Related Risk Considerations.” Amer. J. Ag. Econ. 61(1979):276–84.CrossRefGoogle Scholar
Maddala, G.S.Econometrics. New York: McGraw-Hill Book Company. 1977.Google Scholar
McCarl, B.A. and Rettig, R.B.. “Influence of Hatchery Smolt Releases on Adult Salmon Production and Its Variability.” Canadian J. Fisheries and Aquatic Science 40(1983): 1880–6.CrossRefGoogle Scholar
Mjelde, J.W., Cothern, J.T., Rister, M.E., Hons, E.M., Coffman, C.G., Shumway, C.R., and Lemon, R.G.. “Integrating Data from Various Field Experiments: The Case of Corn in Texas.” J. Prod. Ag. 4(1991):139–147.CrossRefGoogle Scholar
Mjelde, J.W., Griffin, R.C., and Capps, O. Jr. “Empirical Considerations of Heteroscedasticity.” Dept. Information Report DIR 93-1, SP-3, Dept. of Ag. Econ. Texas A&M University. 1993.Google Scholar
Paris, S.J. “A Note on Heteroscedasticity Errors in Regression Analysis.” Review of the International Statistical institute 21(1953):28–9.CrossRefGoogle Scholar
Park, R.E. “Estimation with Heteroscedastic Error Terms.” Econometrica 34(1966):888.CrossRefGoogle Scholar
Yang, S.R., Koo, W.W., and Wilson, W.W.. “Heteroscedasticity in Crop Yield Models.” West. J. Ag. Econ. 17(1992): 103–109.Google Scholar