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Estimating fixed effects: perfect prediction and bias in binary response panel models, with an application to the hospital readmissions reduction program


Kunz, Johannes S; Staub, Kevin E; Winkelmann, Rainer (2017). Estimating fixed effects: perfect prediction and bias in binary response panel models, with an application to the hospital readmissions reduction program. IZA Discussion Paper 11182, University of Zurich.

Abstract

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

Abstract

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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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
JEL Classification:C23, C25, I18
Uncontrolled Keywords:Perfect prediction, bias reduction, penalised likelihood, logit,, , probit, Affordable Care Act
Language:English
Date:November 2017
Deposited On:09 Feb 2018 09:52
Last Modified:31 Jul 2018 04:15
Series Name:IZA Discussion Paper
Number of Pages:44
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://legacy.iza.org/en/webcontent/publications/papers/viewAbstract?dp_id=11182

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