Fixed-Effects estimation of the Linear Discrete-Time Hazard Model: an Adjusted First-Differences Estimator - Tauchmann, Harald (2019)
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
Keywords: linear probability model; individual fixed effects; short panel; discrete-time hazard; duration analysis; survival analysis; non-repeated event; absorbing state; survival bias; misscaling bias