BIAS REDUCTION IN A DYNAMIC REGRESSION MODEL: A Comparison of Jackknifed and Bias Corrected Least Squares Estimators

Jan Kiviet and Garry Phillips

No 293130, University of Amsterdam, Actuarial Science and Econometrics Archive from University of Amsterdam, Faculty of Economics and Business

Abstract: Employing small—sigma asymptotics we approximate the small—sample bias of the ordinary least—squares (OLS) estimator of the full coefficient vector in a linear regression model which includes a one period lagged dependent variable and an arbitrary number of fixed regressors. This bias term is used to construct a corrected ordinary least—squares (COLS) estimator which is unbiased to 0( cr2) . We also consider another technique for bias reduction, viz. jackknifing, and we present a simple expression for the JOLS(m) estimator: the m — delete jackknifed OLS estimator. Then we compare • the accuracy of the 0( cr2) approximation to the bias and the efficiency of OLS, COLS and JOLS(m) in a Monte Carlo study of artificial but realistic models. It is found that the bias is extremely sensitive to the value of a and that COLS can reduce it considerably without undue loss of efficiency if the standard deviation of the OLS lagged dependent variable coefficient estimate has a moderate value.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 45
Date: 1988-10
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Working Paper: BIAS REDUCTION IN A DYNAMIC REGRESSION MODEL: A COMPARISON OF JACKNIFED AND BIAS CORRECTED LEAST SQUARES ESTIMATORS (1988)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:amstas:293130

DOI: 10.22004/ag.econ.293130

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