Approximate bias correction in econometrics

This paper discusses methods for reducing the bias of consistent estimators that are biased in finite samples. These methods are available whenever the bias function, which relates the bias of the parameter estimates to the values of the parameters, can be estimated by computer simulation or by some other method. If so, bias can be reduced by one full order in the sample size and, in some cases that may not be unrealistic, virtually eliminated. Unfortunately, reducing bias may increase the variance, or even the mean squared error, of an estimator. Whether it does so depends on the shape of the bias function. The results 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 estimating a logic model.