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Split-Panel Jackknife Estimation of Fixed-Effect Models

Listed author(s):
  • Geert Dhaene

    (KU Leuven)

  • Koen Jochmans

    (Département d'économie)

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.

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Paper provided by Sciences Po in its series Sciences Po publications with number 2014-03.

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Date of creation: Mar 2014
Handle: RePEc:spo:wpmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l
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