Semiparametric Estimation of Single-Index Transition Intensities
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.
|Date of creation:||Dec 1999|
|Date of revision:|
|Contact details of provider:|| Postal: Øster Farimagsgade 5, Building 26, DK-1353 Copenhagen K., Denmark|
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Web page: http://www.econ.ku.dk
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"Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable,"
95-15, University of Iowa, Department of Economics.
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