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Fixed-effects Estimation of the Linear Discrete-time Hazard Model

其他書名	An Adjusted First-differences Estimator
出版	Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, 2019
URL	http://books.google.com.hk/books?id=hFm6zQEACAAJ&hl=&source=gbs_api

註釋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.