suffer from incidental parameter bias. We show how models with factor structures can also be applied to capture important …

bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function … mean and variance have been derived. Given a closed-form expression for the bias, bias-corrected estimator of the …-parametric and easy to implement. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to …

problem. We develop analytical and jackknife bias corrections for nonlinear models with both individual and time effects … introduce additional incidental parameter bias. As the existing bias corrections apply to models with only individual effects … use of large-T bias adjustments to an important class of models. We also analyze the properties of fixed effects …

sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term …, and bias due to predetermined (as opposed to strictly exogenous) regressors. A bias corrected least squares estimator is … provided. We also present bias corrected versions of the three classical test statistics (Wald, LR and LM test) and show that …

section in Arellano & Bonhomme, 2011) we discuss models with both individual and time effects, split-panel Jackknife bias … incidental parameter bias caused by the estimation of many fixed effects is our main focus, and the unifying theme is that the … order of this bias is given by the simple formula p/n for all models discussed, with p the number of estimated parameters …

bias arising from the presence of noise. Conditions are obtained under which this bias is asymptotically non … the density and quantile function. Our approach can be connected to corrections for selection bias and shrinkage …