Topic：Panel data, fixed effects, and the jackknife
Presenter: Geert Dhaene
Time：March 25, 2017（Saturday）10：00一11：30
Venue: Room 205, Jiageng Building 2
Abstract：It is well known that maximum likelihood estimation of nonlinear panel models with fixed effects produce estimates that suffer from large biases and confidence intervals with poor coverage. The jackknife offers a simple method to reduce this bias and to obtain confidence intervals that are correctly centered under rectangular-array asymptotics. By splitting the panel into blocks, the method is explicitly designed to handle dynamics in the data, and yields estimators that are straightforward to implement and can be readily applied to a range of models and estimands. We provide distribution theory for estimators of model parameters and average effects, present validity tests for the jackknife, and consider extensions to higher-order bias correction and to two-step estimation problems. (Based on joint work with Koen Jochmans, Sciences Po)