Despite the increasing importance of multiple priors in various domains of economics and the significant theoretical advances concerning them, choice-based incentive-compatible multiple-prior elicitation largely remains an open problem. This paper develops a solution, comprising a...

It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is an important task in financial time series analysis. Change point detection is the identification of abrupt...

In this paper, we explore Bayesian inference in models with many instrumental variables that are potentially weakly correlated with the endogenous regressor. The prior distribution has a hierarchical (nested) structure. We apply the methods to the Angrist-Krueger (AK, 1991) analysis of returns...

The impact finance market has sought to 'internalise externalities and adjust risk perceptions' (G20 Green Finance Study Group, 2016), demonstrating the private sector's capability in resolving the climate free-rider problem through the 'greening' of economic activities, partially bypassing...

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...