GMM, GEL, Serial Correlation, and Asymptotic Bias
推导了存在序列相关时GMM和GEL估计量的二阶渐近偏误，发现平滑GEL能消除部分偏误分量，且无序列相关时不必要的平滑和HAC估计反而能降低偏误。
For stationary time series models with serial correlation, we consider generalized method of moments (GMM) estimators that use heteroskedasticity and autocorrelation consistent (HAC) positive definite weight matrices and generalized empirical likelihood (GEL) estimators based on smoothed moment conditions. Following the analysis of Newey and Smith (2004) for independent observations, we derive second order asymptotic biases of these estimators. The inspection of bias expressions reveals that the use of smoothed GEL, in contrast to GMM, removes the bias component associated with the correlation between the moment function and its derivative, while the bias component associated with third moments depends on the employed kernel function. We also analyze the case of no serial correlation, and find that the seemingly unnecessary smoothing and HAC estimation can reduce the bias for some of the estimators. Copyright The Econometric Society 2005.