TRUNCATED REGRESSION IN EMPIRICAL ESTIMATION

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.


Issue Date:
2000
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/36391
Total Pages:
22
Series Statement:
Selected Paper of the 2000 Annual Meeting, June 29-July 1, 2000, Vancouver, British Columbia




 Record created 2017-04-01, last modified 2017-08-25

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