Dhaene, G. and Jochmans, K.

Split-panel Jackknife Estimation of Fixed-Effect Models

Review of Economic Studies

Vol. 82(3) pp. 991–1030 (2015)

Abstract: Maximum-likelihood estimation of nonlinear models with fixed effects is subject to the incidental-parameter problem. This typically implies that point estimates suffer from large bias and confidence intervals have poor coverage. This article presents a jackknife method to reduce this bias and to obtain confidence intervals that are correctly centred under rectangular-array asymptotics. 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. An empirical illustration relating to female labour-force participation is also provided.

Keywords: Bias reduction, Dependent data, Incidental-parameter problem, Jackknife, Nonlinear model

JEL Codes: C13, C14, C22, C23

Author links:

Publisher's Link: https://doi.org/10.1093/restud/rdv007