censored population. We then correct the derivative for the effects of the selection bias. We propose nonparametric and …

have smaller bias that is flatter as a function of first step smoothing leading to improved small sample properties. Series …

Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias … overcome this problem it is common practice to either undersmooth, so as to reduce the impact of bias, or oversmooth, and … thereby introduce an explicit or implicit bias estimator. However, these approaches, and others based on nonstandard smoothing …

We consider the bias of the 2SLS estimator in the linear instrumental vari-ables regression with one endogenous … regressor only. By using asymptotic expansion techniques we approximate 2SLS coefficient estimation bias under various scenarios … regarding the number and strength of instruments.The resulting approximation encompasses existing bias approximations, which are …

This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time …

SLS can exhibit substantial finite sample (second-order) bias when the model is over-identified and the first stage … estimates in this situation. Morimune (1983) analyzed both the bias in 2SLS and the lack of moments in LIML. While it was long … jackknife 2SLS (J2SLS) estimator that attenuated the 2SLS bias problem and had good dispersion properties. They found in their …

that such bias-reducing weights must depend on the data unless an orthogonal reparameterization or an essentially …. Finally, we show that random effects estimators are not bias reducing in general and discuss important exceptions. Three …

bias and so are important when the first step is machine learning. We derive LR moment conditions for dynamic discrete …