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...

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 introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and...

We use survival analysis to analyse the impact of export credit guarantees on firms' export duration using granular Swedish panel data at the firm-country and firm-country-product levels. The estimation results show that firms' export survival substantially increases with guarantees, at both...

I expose the risk of false discoveries in the context of multiple treatment effects. A false discovery is a nonexistent effect that is falsely labeled as statistically significant by its individual t-value. Labeling nonexistent effects as statistically significant has wide-ranging academic and...

This paper examines the empirical analysis of treatment effects on duration outcomes from data that contain instrumental variation. We focus on social experiments in which an intention to treat is randomized and compliance may be imperfect. We distinguish between cases where the treatment starts...

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...