One of the key motivations in the construction of ever more sophisticated mortality models was the realisation of the importance of “cohort effects” in the historical data. However, these are often difficult to estimate robustly, due to the identifiability issues present in age/period/cohort...

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these...

This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but...

This paper considers an infinitely repeated three-player Bayesian game with lack of information on two sides, in which an informed player plays two zero-sum games simultaneously at each stage against two uninformed players. This is a generalization of Aumann, Maschler and Stearns (1995)...

This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain...

This paper considers multiple market agents who have distinct distributional opinions about the state price density. Different opinions can be contested on a hypothetical market that trades Arrow-Debreu securities. We focus on the situation when the agents are maximizing logarithmic utility as...

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for "online" estimation (that...

Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data,...