Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the...

We reanalyze the effects of a Danish active labour market program social experiment, that included a range of sub-treatments, including monitoring, job search assistance and training. Previous studies have shown that the overall effect of the experiment is positive. We apply newly developed...

This paper considers the definition and identification of treatment effects on conditional transition probabilities. We show that even under sequential random assignment only the instantaneous average treatment effect is point identified. Because treated and control units drop out at different...

We test if social work norms are important for work absence due to self-perceived sick-ness. To this end, we use a randomized social experiment designed to estimate the effect of monitoring on work absence. The treated were exposed to less monitoring of their eligibility to use sickness...

We consider the estimation of nonlinear models with mismeasured explanatory variables, when information on the marginal distribution of the true values of these variables is available. We derive a semi-parametric MLE that is shown to be consistent and asymptotically normally distributed. In a...

This paper builds on the Empirical Monte Carlo simulation approach developed by Huber et al. (2013) to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job-seekers with information on participation in a training program to simulate placebo...

Unemployment insurance systems include monitoring of unemployed workers and punitive sanctions if job search requirements are violated. We analyze the effect of sanctions on the ensuing job quality, notably on wage rates and hours worked, and we examine how often a sanction leads to a lower...

We study the short-run and long-run economic impact of one of the largest losses that an individual can face; the death of a child. We utilize unique merged registers on the entire Swedish population, combining information on the date and cause of death with parents' labour market outcomes, health...

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...

Consider an observed binary regressor D and an unobserved binary variable D*, both of which affect some other variable Y. This paper considers nonparametric identification and estimation of the effect of D on Y , conditioning on D* = 0. For example, suppose Y is a person's wage, the unobserved D...