Iterative Bias Correction Procedures Revisited: A Small Scale Monte Carlo Study
No 15-02, UvA-Econometrics Working Papers from Universiteit van Amsterdam, Dept. of Econometrics
This paper considers estimation of general panel data models subject to the incidental parameter problem of Neyman and Scott (1948). Our main focus is on the finite sample behavior of analytical bias corrected Maximum Likelihood estimators as discussed in Hahn and Kuersteiner (2002), Hahn and Newey (2004) and Hahn and Kuersteiner (2011). As it is mentioned in Hahn and Newey (2004) and Arellano and Hahn (2006), in principle it is possible to iterate the bias formula to obtain an estimator that might have better finite sample properties than the one step estimator. In this paper we will investigate merits and limitations of iterative bias correction procedures in finite samples, by considering three examples: Panel AR(1), Panel VAR(1) and Static Panel Probit.
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