Split-panel Jackknife Estimation of Fixed-effect Models

2015/02/12 by Geert Dhaene, Koen Jochmans · 10 citations
Economics, Econometrics and Finance · #Spatial and Panel Data Analysis #Housing Market and Economics #Economic and Environmental Valuation

paper · doi:10.1093/restud/rdv007

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.

Citations

• Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations
• Bias reduction of maximum likelihood estimates
• Biases in Dynamic Models with Fixed Effects

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