The availability of Big Data to model economic decisions promises to revolutionize our understanding of how individual choices translate into social welfare, and develop economic policies which take into account the complexity of human preferences and the heterogeneous impact of policy interventions across the full socio-demographic spectrum. For the first time it is possible for us to accurately track individual behavior across a range of dimensions and over time and understand how the multitude of individual choices are shaped by environments, preferences and social networks, and what the eventual consequences of these choices are for individuals and for society at large.
The aim of this blog is to highlight recent developments in Big Data Econometrics and provide both theoretical and practical insights for the applied practitioner.
Prof. Harding is an Assistant Professor in the Department of Economics at Stanford University and a SIEPR Faculty Fellow. His aim is to understand how individuals make consumption choices in a data rich environment, and quantify the individual and social welfare impact of their choices in areas such as Energy/Environment and Health/Nutrition. Building on a rigorous foundation in econometric methods, Dr. Harding relies on terabyte sized data sets on household energy consumption, food purchases, and prescription records to build a comprehensive framework for understanding economic choices. He also develops cutting edge econometric methods for the practical analysis of Big Data econometric models using Bayesian and Quantile techniques, by focusing in particular on the role of unobserved heterogeneity in complex massive data.
Danton is a fourth year PhD student in the Department of Economics at Georgetown University, specializing in Applied Econometrics. He’s been working with Prof. Harding as a teaching and/or research assistant for two years. When he’s not coding for Dr. Harding, he’s working on his own research projects in education, energy, and health/income/food inequality.