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 … mimic panel estimates. We create two metrics for assessing the grouping process, one for each potential source of bias. If …

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case …

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case …

factors explaining foreign investors' decisions. However, sampling proves fundamental as these overall determinants mask deep …) is crucial in foreign investor's decisions. The main message is that minimising sampling biases and accounting for …

estimate its bias is widely accepted. However, when we apply it to entropy estimator constructed by "plug-in" method, we have …

In this paper, I present a simple characterization of the sample selection bias problem that is also applicable to the … problem of sample selection bias is fit within the conventional specification error framework of Griliches and Theil. A simple … estimator is discussed that enables analysts to utilize ordinary regression methods to estimate models free of selection bias …

We derive the finite sample bias of the sample cross-covariance estimator based on a stationary vector-valued time … series with an unknown mean. This result leads to a bias-corrected estimator of cross-covariances constructed from linear … combinations of sample cross-covariances, which can in theory correct for the bias introduced by the first $h$ lags of cross …