We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We use this approach in the context of stock return predictability and...

This paper shows how the dynamic linear model with fixed regressors can be efficiently estimated. This dynamic model can be used to distinguish spurious correlation from state dependence and we show that the integrated likelihood estimator is adaptive for any asymptotics with T increasing where...

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 examines the properties of G-7 cycles using a multicountry Bayesian panel VAR model with time variations, unit specific dynamics and cross country interdependences. We demonstrate the presence of a significant world cycle and show that country specific indicators play a much smaller...

The global financial crisis has led to a revival of the empirical literature on current account imbalances. This paper contributes to that literature by investigating the importance of evaluating model and parameter uncertainty prior to reaching any firm conclusion. We explore three alternative...

This paper examines two competing approaches for calculating current account benchmarks, i.e. the external sustainability approach á la Lane and Milesi-Ferretti (LM) versus the structural current accounts literature (SCA) based on panel econometric techniques. The aim is to gauge the medium...

This paper considers a linear panel data model with time varying heterogeneity. Bayesian inference techniques organized around Markov chain Monte Carlo (MCMC) are applied to implement new estimators that combine smoothness priors on unobserved heterogeneity and priors on the factor structure of...

We utilize Bayesian model averaging to estimate a stochastic discount factor (SDF) for single-stock options. A Bayesian model averaging SDF outperforms reduced-form benchmark models in-sample and out-of-sample in pricing option return anomalies and portfolios. We document that the SDF is dense...

This paper investigates in how far monetary policy shocks impact Euro- pean asset markets, conditional on different risk states. It focuses on four different asset classes: equity of industrial firms, equity of banks, high-grade corporate bonds, and high-yielding corporate bonds. We distinguish...

We use Bayesian techniques to select factors in a general multifactor asset pricing model. From a given set of 15 factors we evaluate all possible pricing models by the extent to which they describe the data as given by the posterior model probabilities. Interest rates, premiums, returns on...