School of Civil and Environmental Engineering Cornell University
April 25, 2019, 10:00, Room GC B1 10 (click here for the map)
Bayes estimators of the parameters of choice models offer several advantages over the dominant maximum likelihood approach. Although Bayesian techniques are the norm in some choice modeling fields, such as marketing, in other fields there has been some resistance to the use of Bayesian econometrics. In this talk, the fundamental principles behind computational Bayesian statistics will be reviewed before stressing associated benefits of Bayesian tools such as the use of predictive posteriors and credible intervals, estimators that are integral, gradient, and Hessian free, treatment of latent variables, and direct inference on transformation of the parameters of interest via post-processing Monte Carlo Markov chains. In addition to discussing implementation with empirical case studies, common misunderstandings will be clarified.
Ricardo Daziano, choice modeler and PhD in economics (Universite Laval), is an associate professor of Civil and Environmental Engineering at Cornell University. Daziano's research focuses on engineering decision making, specifically on non-market valuation and choice microeconometrics applied to technological innovation in transportation and energy transitions. One of his goals is to better understand the interplay of consumer behavior and engineering, investment, and policy choices for broad adoption of energy efficiency.