Split-panel jackknife estimation of fixed-effect models

Overview & Synopsis

We propose a jackknife for reducing the order of the bias of maximum likelihood estimates of nonlinear dynamic fixed-effect panel models. In its simplest form, the half-panel jackknife, the estimator is just 2θb− θ1/2 , where θb is the MLE from the full panel and θ1/2 is the average of the two half-panel MLEs, each using T /2 time periods and all N cross-sectional units. This estimator eliminates the first-order bias of θb. The order of the bias is further reduced if two partitions of the panel are used, for example, two half-panels and three 1/3-panels, and the corresponding MLEs. On further partitioning the panel, any order of bias reduction can be achieved. The split-panel jackknife estimators are asymptotically normal, centered at the true value, with variance equal to that of the MLE under asymptotics where T is allowed to grow slowly with N. In analogous fashion, the split-panel jackknife reduces the bias of the profile likelihood and the bias of marginal-effect estimates. Simulations in fixed-effect dynamic discrete-choice models with small T show that the split-panel jackknife effectively reduces the bias and mean-squared error of the MLE, and yields confidence intervals with much better coverage