Many recent papers in macroeconomics have used large Vector Autoregressions (VARs) involving a hundred or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital in achieving reasonable results. Computational concerns currently limit the range of priors...

This paper develops semiparametric Bayesian methods for inference in dynamic linear panel data models, and applies them to longitudinal data on labor earnings from the Panel Study of Income Dynamics. We focus on characterizing not only parameters related to conditional means and variances, but...

The primary emphasis of quantitative marketing is on the evaluation of firm policies for determination of marketing variables as well as the optimization of these policies. Thus, our methods of inference must apply to decision problems and to policy evaluation. In addition, the data we have...

This chapter provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.

This paper posits a new framework to model the trial of and repeat purchasing of a new product. While much research has examined underlying shifts in consumer purchasing patterns, the typical assumption has been that the underlying purchasing process remains the same although the purchasing...

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...

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...

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We...