Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/206572
Authors: 
Tauchmann, Harald
Year of Publication: 
2019
Series/Report no.: 
FAU Discussion Papers in Economics 09/2019
Abstract: 
This paper shows that popular linear fixed-effects panel-data estimators (first-differences, within-transformation) are biased and inconsistent when applied in a discrete-time hazard setting, that is, one with the outcome variable being a binary dummy indicating an absorbing state, even if the data generating process is fully consistent with the linear discrete-time hazard model. Besides conventional survival bias, these estimators suffer from another source of - potentially severe - bias that originates from the data transformation itself and is present even in the absence of any unobserved heterogeneity. We suggest an alternative, computationally very simple, adjusted first-differences estimator that cures the data-transformation driven bias of the classical estimators. The theoretical line of argument is supported by evidence from Monte Carlo simulations and is illustrated by an empirical application.
Subjects: 
linear probability model
individual fixed effects
short panel
discrete-time hazard
duration analysis
survival analysis
non-repeated event
absorbing state
survival bias
misscaling bias
JEL: 
C23
C25
C41
Document Type: 
Working Paper
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