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Small-Sample Evaluation of Mean-Variance Production Function Estimators

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  • Steven T. Buccola
  • Bruce A. McCarl

Abstract

Production functions have been shown useful for characterizing input effects on both the mean and variability of yield. Monte carlo experiments are used here to investigate small-sample properties of selected mean-variance production function estimators. Estimation efficiency in the mean is found to improve with iteration on the mean and variance components. Although efficiency in the variance is greatest at the first stage, bias in the variance diminishes through at least the second stage. These effects are influenced by the degree and sign of heteroscedasticity and by sample size.

Suggested Citation

  • Steven T. Buccola & Bruce A. McCarl, 1986. "Small-Sample Evaluation of Mean-Variance Production Function Estimators," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(3), pages 732-738.
    • Handle: RePEc:oup:ajagec:v:68:y:1986:i:3:p:732-738.
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    File URL: http://hdl.handle.net/10.2307/1241558
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    Cited by:

    1. Atanu Saha & Arthur Havenner & Hovav Talpaz, 1997. "Stochastic production function estimation: small sample properties of ML versus FGLS," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 459-469.
    2. Love, H. Alan & Buccola, Steven T., 1989. "Risk Aversion, Input Use, And Heteroskedastic Supply," 1989 Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk Meeting, April 9-12, 1989, Sanibel Island, Florida 271525, Regional Research Projects > S-232: Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk.
    3. Buccola, Steven T. & Sukume, Chrispen, 1987. "Pricing And Storage Decisions In Government Grain Marketing Boards," 1987 Annual Meeting, August 2-5, East Lansing, Michigan 269978, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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