We estimate the distribution of television advertising elasticities and the distribution of the advertising return on investment (ROI) for a large number of products in many categories. Our results reveal substantially smaller advertising elasticities compared to the results documented in the...

Given the extreme dependence of agriculture on weather conditions, this paper analyses the effect of climatic variations on this economic sector, by considering both a huge dataset and a flexible spatio-temporal model specification. In particular, we study the response of N-fertilizer...

The experience of past financial market turmoil suggests that in addition to eroding investor wealth, the severe consequences of rare extreme market events can spillover and impair the broader real economies. In this context, this paper is an evaluation of the methodological and empirical...

Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC)...

Imagination is more important than knowledge, but if intellect does not provide the needed logical structures, capacities for envisioning new possibilities are overly constrained. The sustainability problems we face today cannot be solved with the same kind of thinking that created them, but...

This paper develops a framework for estimating Quantal Response Equilibrium models from experimental data using Bayesian techniques. Bayesian techniques offer some advantages over the more commonly-used maximum likelihood approach: (i) the accuracy of the posterior simulation is limited by...

This paper builds on a previous paper that evaluates a spatial hedonic difference-in-differences model to estimate the effects on home prices of a public housing project in North Dallas. Here, we perform a spatial falsification exercise by simulating the spatial GMM estimation on over 5,000...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample t can be a poor guide to actual forecasting e ffectiveness. But post-sample model testing requires an often-consequential a priori partitioning of the data into an `in-sample' period...