In this paper, I show that the "Bayesian inversion" of demand, which is defined as the posterior distribution of random utilities given realized choices, can replace Berry, Levinsohn, and Pakes (1995)’ "mean utility inversion" and radically simplify the estimation of discrete choice models...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the...

We investigate how an informed designer maximizes her objective when facinga player whose payoff depends on both the designer's private information andon an unknown state within the classical quasilinear environment. Thedesigner can disclose arbitrary information about the state via...

The extent to which push and pull factors affect international capital flows is widely debated. We contribute to this strand of literature by estimating the relative importance of push and pull factors for portfolio flows over a time span, encompassing the global financial crisis, the European...

Many marketing research projects aim not only to estimate the causal effect of an intervention but also to understand the mechanism by which the intervention affects outcomes. However, existing causal inference methods, such as difference-in-differences and synthetic control, do not include...

Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a...

Missing time-series data is a prevalent problem in finance. Imputation methods for time- series data are usually applied to the full panel data with the purpose of training a model for a downstream out-of-sample task. For example, the imputation of missing returns may be applied prior to...

The reliability of the mercury spallation target is mission-critical for the neutron science program of the spallation neutron source at the Oak Ridge National Laboratory. We present an inverse uncertainty quantification (UQ) study using the Bayesian framework for the mercury equation of state...

The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is...