Approximate Bias Correction In Econometrics

James MacKinnon and P. Smith
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P. Smith: Queen's University

No 919, Working Paper from Economics Department, Queen's University

Abstract: This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is necessary that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some other method. If so, bias can be reduced or, in some cases that may not be unrealistic, even eliminated. In general, several evaluations of the bias function will be required to do this. Unfortunately, reducing bias may increase the variance, or even the mean squared error, of an estimator. Whether or not it does so depends on the shape of the bias functions. The techniques of the paper are illustrated by applying them to two problems: estimating the autoregressive parameter in an AR(1) model with a constant term, and estimation of a logit model.

Keywords: finite samples; bias function; mean squared error; simulation (search for similar items in EconPapers)
JEL-codes: C13 (search for similar items in EconPapers)
Pages: 28 pages
Date: 1995-01
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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_919.pdf First version 1995 (application/pdf)

Related works:
Journal Article: Approximate bias correction in econometrics (1998)
Working Paper: Approximate Bias Correction in Econometrics (1996)
Working Paper: Approximate Bias Correction in Econometrics
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:919

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