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Truncated Regression In Empirical Estimation

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  • Marsh, Thomas L.
  • Mittelhammer, Ronald C.

Abstract

In this paper we illustrate the use of alternative truncated regression estimators for the general linear model. These include variations of maximum likelihood, Bayesian, and maximum entropy estimators in which the error distributions are doubly truncated. To evaluate the performance of the estimators (e.g., efficiency) for a range of sample sizes, Monte Carlo sampling experiments are performed. We then apply each estimator to a factor demand equation for wheat-by-class.

Suggested Citation

  • Marsh, Thomas L. & Mittelhammer, Ronald C., 2000. "Truncated Regression In Empirical Estimation," 2000 Annual Meeting, June 29-July 1, 2000, Vancouver, British Columbia 36391, Western Agricultural Economics Association.
  • Handle: RePEc:ags:waeava:36391
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    File URL: http://purl.umn.edu/36391
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    References listed on IDEAS

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    1. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
    2. Geweke, John, 1989. "Exact predictive densities for linear models with arch disturbances," Journal of Econometrics, Elsevier, vol. 40(1), pages 63-86, January.
    3. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    4. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    5. Alice Nakamura & Masao Nakamura, 1983. "Part-Time and Full-Time Work Behaviour of Married Women: A Model with a Doubly Truncated Dependent Variable," Canadian Journal of Economics, Canadian Economics Association, vol. 16(2), pages 229-257, May.
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