Split-Panel Jackknife Estimation of Fixed-Effect Models

Geert Dhaene and Koen Jochmans

SciencePo Working papers from HAL

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 paper presents a jackknife method to reduce this bias and to obtain confidence intervals that are correctly centered under rectangular-array asymptotics. The method is explicitly designed to handle dynamics in the data and yields estimators that are straightforward to implement and that can be readily applied to a range of models and estimands. We provide distribution theory for estimators of index coefficients 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 on female labor-force participation is also provided.

Keywords: bias reduction; dependent data; incidental-parameter problem; jackknife; nonlinear model (search for similar items in EconPapers)
Date: 2014-03
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-01070553
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Split-panel Jackknife Estimation of Fixed-effect Models (2015)
Working Paper: Split-panel jackknife estimation of fixed-effect models (2015)
Working Paper: Split-panel jackknife estimation of fixed-effect models (2015)
Working Paper: Split-Panel Jackknife Estimation of Fixed-Effect Models (2014)
Working Paper: Split-Panel Jackknife Estimation of Fixed-Effect Models (2014)
Working Paper: Split-panel jackknife estimation of fixed-effect models (2010)
Working Paper: Split-panel jackknife estimation of fixed-effect models (2010)
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