The paper evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting two applications. The approach is due to Ferguson (1973, 1974) and Rubin (1981). Our first application considers an educational choice problem. We focus on obtaining a predictive distribution for...

Approximate Bayesian Computation (ABC) provides Monte Carlo inference of the posterior distribution, even for models with intractable likelihoods. The quality of ABC inference relies on the choice of tolerance for the distance between the observed data summary statistics, and the summary...

This article discusses the portfolio selection problem from a Bayesian perspective. In doing so, I first provide an overview of the portfolio problem and motivate the decision-making process from an expected utility point of view. Then, I demonstrate the analytical solution to the problem and...

This paper incorporates Bayesian estimation and optimization into portfolio selection framework, particularly for high-dimensional portfolio in which the number of assets is larger than the number of observations. We leverage a constrained 𝓁1 minimization approach, called linear programming...

In the spirit of the DSGE-VAR approach, I employed Temporal Difference Variational Auto-Encoder (TDVAE) proposed by Gregor et al. (2019) as a reduced timeseries model on which theoretical restrictions from a DSGE model are imposed. TDVAE is virtually a general-form stochastic state-space model...

Abstract This article proposes a distributed Markov chain Monte Carlo (MCMC) algorithm for estimating Bayesian hierarchical models when the panel size is extremely large (in the millions of consumers) and the objects of interest are the distribution of heterogeneity and the parameters that...

The study is empirically motivated to analyze the performance of new class of Bayesian shrinkage priors that are powerful in reducing time-varying parameters to static ones to avoid over-fitting problem in time-varying parameter models. We utilized newly improved shrinkage priors in a...

This paper uses an Empirical Bayes approach to estimate the conditional mean function of a Poisson regression model, evaluated at counterfactual values of time-invariant unobserved covariates. The application that motivates and illustrates the methods developed herein is the use of deadly force...

We contribute to research on mixed-frequency regressions by introducing an innovative Bayesian approach. We impose a Normal-inverse Wishart prior by adding a set of auxiliary dummies in estimating a Mixed-Frequency VAR. Based on this new “high-frequency” identification scheme, we illustrate...

It is well known that estimated mean-variance portfolios deliver, on average, poor out-of-sample performance. A lesser-known fact that we characterize in this paper is that their out-of-sample performance is also very volatile. Using our analytical characterization of out-of-sample performance...