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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Estimating fixed effects: perfect prediction and bias in binary response panel models, with an application to the hospital readmissions reduction program
Organization Unit
  • Johannes Kunz
  • Kevin E Staub
  • Rainer Winkelmann
  • English
Institution University of Zurich
Series Name IZA Discussion Paper
Number 11182
Number of Pages 44
Date 2017
Abstract Text The maximum likelihood estimator for the regression coefficients, β, in a panel binary response model with fixed effects can be severely biased if N is large and T is small, a consequence of the incidental parameters problem. This has led to the development of conditional maximum likelihood estimators and, more recently, to estimators that remove the O(T–1) bias in β^. We add to this literature in two important ways. First, we focus on estimation of the fixed effects proper, as these have become increasingly important in applied work. Second, we build on a bias-reduction approach originally developed by Kosmidis and Firth (2009) for cross-section data, and show that in contrast to other proposals, the new estimator ensures finiteness of the fixed effects even in the absence of within-unit variation in the outcome. Results from a simulation study document favourable small sample properties. In an application to hospital data on patient readmission rates under the 2010 Affor
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Keywords Perfect prediction, bias reduction, penalised likelihood, logit,, , probit, Affordable Care Act