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 …

There exists a small sample bias in predictive regressions, when a rate of return is regressed on a lagged stochastic … regressor, and the regression disturbance is correlated with the regressors' innovations. Although this bias can be a serious … level data, we document that as the number of cross sections used in the panel data increases the bias in coefficient …

corrected versions of it are comparedwith IV and GMM regarding: coefficient bias, accuracy of varianceestimators - both of the … disturbances and of the coefficientestimators - and the actual size of coefficient tests. A reasonablysimple and consistent bias …

The relative magnitudes are compared of successive terms in a higher-order asymptotic expansion of the bias of the LSDV … estimator in dynamic panels. We find that the leading term accounts for the major part of the actual bias in small samples. This … implies that bias correction procedures can be based on relatively simple bias approximation formulas. …

For linear panel data models with fixed effects and sample selection, we correct for selectivity bias in the within …

Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias … estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are … estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or …