Researchers typically use rank ordered logit regression to analyze consumer preference data collected with Best-Worst Scaling (BWS) surveys. We propose a generalized rank ordered logit (GROL) model that accommodates scale and preference heterogeneity. Both the GROL and mixed rank ordered logit...

In this paper we reconsider large Bayesian Vector Autoregressions (BVAR) from the point of view of Bayesian Compressed Regression (BCR). First, we show that there are substantial gains in terms of out-of-sample forecasting by treating the problem as an error-in-variables formulation and...

This article proposes methods for estimating a Bayesian vector autoregression (VAR) model with an informative steady state prior which also accounts for possible structural changes in the long-term trend of the macroeconomic variables. I show that, overall, the proposed time-varying steady state...

This study proposes methods for estimating Bayesian vector autoregressions (VARs) with an automatic variable selection and an informative prior on the unconditional mean or steady-state of the system. We show that extant Gibbs sampling methods for Bayesian variable selection can be efficiently...

In this article, we present a new perspective on forecasting technology adoption, focused on the extensive margin of adoption of multiple digital technologies in multiple countries. We do this by applying a Bayesian hierarchical structure to the seminal model of technology diffusion. After...

During an epidemic, the information available to individuals in the society deeply influences their belief of the epidemic spread, and consequently the preventive measures they take to stay safe from the infection. In this paper, we develop a scalable framework for ascertaining the optimal...

Building on a proper selection of macroeconomic variables for constructing a GDP forecasting multivariate model (Kazanas, 2017), this paper evaluates whether alternative Bayesian model specifications can provide greater forecasting accuracy compared to a standard VECM model. To that end, two...

We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory and marketing expenditures. We introduce a unified, robust, and...

How does lie detection constrain the potential for one person to persuade another to change her action? We consider a model of Bayesian persuasion in which the Receiver can detect lies with positive probability. We show that the Sender lies more when the lie detection probability increases. As...

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...