A general formulation of Mixed Proportional Hazard models with K random effects is provided. It enables to account for a population stratified at K different levels. We then show how to approximate the partial maximum likelihood estimator using an EM algorithm. In a Monte Carlo study, the...

Using a proportional hazard model with multiple exits, this paper analyzes whether immigrants' unemployment spells differ from natives', and if so, how the difference vary with time spent in Sweden and across immigrant cohorts. A unique data set taken from the Swedish unemployment registers is...

Using a proportional hazard model with multiple exits, this paper analyzes whether immigrants' unemployment spells differ from natives', and if so, how the difference vary with time spent in Sweden and across immigrant cohorts. A unique data set taken from the Swedish unemployment registers is...

This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that the duration framework in discrete time provides a fertile ground for effect evaluations. We suggest easy-to-use nonparametric survival function...

The vocational employment training program is the most expensive training program in Sweden and a cornerstone of labor market policy. We analyze its causal effects on the individual transition rate from unemployment to employment by exploiting variation in the timing of treatment and outcome,...

The vocational employment training program is the most expensive training program in Sweden and a cornerstone of labor market policy. We analyze its causal effects on the individual transition rate from unemployment to employment by exploiting variation in the timing of treatment and outcome,...

In a large class of hazard models with proportional unobserved heterogeneity, the distribution of the heterogeneity among survivors converges to a gamma distribution. This convergence is often rapid. We derive this result as a general result for exponential mixtures and explore its implications...

This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that the duration framework in discrete time provides a fertile ground for effect evaluations. We suggest easy-to-use nonparametric survival function...

We propose a simple procedure based on an existing “debiased” l_{1}-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number...

The most familiar fixed effects (FE) and random effects (RE) panel data treatments for count data were proposed by Hausman, Hall and Griliches (HHG) (1984). The Poisson FE model is particularly simple and is one of a small few known models in which the incidental parameters problem is, in fact,...