Semiparametric Estimation of Single-Index Transition Intensities

Abstract

This research develops semiparametric kernel-based estimators of state-specific conditional transition intensitiesm, hs (y|x), for duration models with right-censoring and/or multiple destinations (competing risks). Both discrete and continous duration data are considered. The maintained assumptions are that hs(y|x) depends on x only through an index x'Bs. In contrast to existing semiparametric estimators, proportional intensities is not assumed. The new estimators are asymptotically normally distributed. The estimator of Bs is root-n consistent. The estimator of hs (y|x) achieves the one-dimensional rate of convergence. Thus the single-index assumption eliminates the "curse of dimensionality". The estimators perform well in Monte Carlo experiments.semiparametric estimation; kernel regression; duration analysis; competing risks; censoring