Pseudo-panels allow estimation of panel models when only repeated cross-sections are available. This involves grouping … constrained by a lack of consensus on how the pseudo-panels should be formed, particularly to address potential sampling error … bias. We show that grouping can also create substantial aggregation bias, calling into question how well pseudo-panels can …

gains of imposing a (valid) exclusion restriction are quite small, particularly with respect to the estimation of marginal …

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data …. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate that the estimator has good …

estimator is biased in the context of dynamic estimation. The estimators taking into account the resulting bias can be grouped … broadly into the class of instrumental estimators and the class of direct bias corrected estimators. The simulation results … clearly favour the direct bias corrected estimators, especially the estimator proposed by Hansen (2001). The superiority of …

estimator is biased in the context of dynamic estimation. The estimators taking into account the resulting bias can be grouped … broadly into the class of instrumental estimators and the class of direct bias corrected estimators. The simulation results … clearly favour the direct bias corrected estimators, especially the estimator proposed by Hansen (2001). The superiority of …

analyst faces a sample selection bias problem. The aim of this paper is to provide further evidence using a simple sequential … different levels of estimation and diagnostic with the Ordinary Least Squares (OLS), Heckman’s 2-step and Full Information … with sample selection bias than the Heckman’s 2-step approach. …

) bias in β^. We add to this literature in two important ways. First, we focus on estimation of the fixed effects proper, as … these have become increasingly important in applied work. Second, we build on a bias-reduction approach originally developed …

mechanism may differ among the levels of nonresponse, and merging the levels of nonresponse may cause bias in the results of the …

Estimations of quadratic forms in the parameters of linear models exhibit small-sample bias. The direct computation for … a bias correction is not feasible when the number of covariates is large. We propose a bootstrap method for correcting … this bias that accommodates different assumptions on the structure of the error term including general heteroscedasticity …