Finite-sample bias in free energy bridge estimators

Finite sample properties of multiple imputation estimators

Finite sample properties of multiple imputation estimators under the linear regression model are studied. The exact bias of the multiple imputation variance estimator is presented. A method of reducing the bias is presented and simulation is used to make comparisons. We also show that the suggested method can be used for a general class of linear estimators. 1. Introduction. Multiple imputation...

Bridge Homogeneous Volatility Estimators

Finite Sample Properties of Semiparametric Estimators of Average Treatment Effects∗

We explore the finite sample properties of several semiparametric estimators of average treatment effects, including propensity score reweighting, matching, double robust, and control function estimators. When there is good overlap in the distribution of propensity scores for treatment and control units, reweighting estimators are preferred on bias grounds and attain the semiparametric efficien...

Alternative HAC covariance matrix estimators with improved finite sample properties

HAC estimators are known to produce test statistics that reject too frequently in finite samples. One neglected reason comes from using the OLS residuals when constructing the HAC estimator. If the regression matrix contains high leverage points, such as from outliers, then the OLS residuals will be negatively biased. This reduces the variance of the OLS residuals and the HAC estimator takes th...

On the finite sample behavior of adaptive estimators

We explore the finite sample performance of adaptive estimators in linear time series regression models. Our results show that for samples of only 50 observations, the 90 percent confidence interval of the adaptive estimator is 20 to 50 percent smaller than the corresponding interval for its GLS counterpart across a range of symmetrical distributions. When the assumption of symmetry is relaxed ...