Predicting fixed effects in panel probit models
Johannes Kunz (),
Kevin Staub () and
Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York
We present a method to estimate and predict fixed effects in a panel probit model when N is large and T is small, and when there is a high proportion of individual units without variation in the binary response. Our approach builds on a bias-reduction method originally developed by Kosmidis and Firth (2009) for cross-section data. In contrast to other estimators, our approach ensures that predicted fixed effects are finite in all cases. Results from a simulation study document favorable properties in terms of bias and mean squared error. The estimator is applied to predict period-specific fixed effects for the extensive margin of health care utilization (any visit to a doctor during the previous three months), using German data for 2000-2014. We find a negative correlation between fixed effects and observed characteristics. Although there is some within-individual variation in fixed effects over sub-periods, the between-variation is four times as large.
Keywords: Perfect prediction; Bias reduction; modified score function (search for similar items in EconPapers)
JEL-codes: I11 I18 C23 C25 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:yor:hectdg:18/23
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