allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian … forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with …

We develop a method to identify the individual latent propensity to select into treatment and marginal treatment … of their treatment-contingent outcomes. We use the method to study how child birth affects female labor supply in Denmark … on the treated computed using our method and traditional event-study methods are nearly equal. Finally, we study the …

We propose a novel Bayesian Graded Response Model (BGRM) for food security measurement. Our BGRM has several attractive … features. It produces continuous food security estimates and measures of estimation uncertainty at the household level. Unlike …

advanced ones. Notably, we document a key asymmetry: fragmentation has an immediate negative effect, while the benefits of …

We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and …

combination of differentiable state-space models and the Hamiltonian Monte Carlo (HMC) sampler. First, we introduce a method for …-order approximations of DSGE models. The availability of derivatives also enables a general filter-free method to estimate nonlinear, non …

new decision trees) and locally selects variables (with Bayesian priors) for modeling panel data with potential grouped … heterogeneity. We derive analytical marginal likelihoods as global split criteria in our Bayesian Clustering Model (BCM), to …