This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that, without additional assumptions, it is not possible to estimate the average treatment effect and treatment on the treated. It is, however,...

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,...

This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that, without additional assumptions, it is not possible to estimate the average treatment effect and treatment on the treated. It is, however,...

Using a unique micro panel data set we investigate whether active labor market programs improve employment prospects and increase mobility in the longer run. We consider two prototype programs: job creation programs and training programs. We find that both programs reduce the chances of finding...

This paper investigates how precisely short-term, job-search oriented training programs as opposed to long-term, human capital intensive training programs work. We evaluate and compare their effects on time until job entry, stability of employment, and earnings. Further, we examine the...

Using a unique micro panel data set we investigate whether active labor market programs improve employment prospects and increase mobility in the longer run. We consider two prototype programs: job creation programs and training programs. We find that both programs reduce the chances of finding...

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and...

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature, the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given...

In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution for this skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet process...