CAREER: Bayesian Econometric Modeling and Nonparametric Identification

This project aims to introduce and improve methods for analyzing economic data and robustly assessing the likely impact of policies, and to develop new course material to train graduate students in the analysis of economic data. The main approach centers around Bayesian estimation methods applied to large parameter spaces, in combination with recent strategies for defining and identifying effects of treatments or policies.

The first component of this project will develop new methodology for estimation of flexible models for economic data by adapting Bayesian methods for inference to handle estimation problems in which prior knowledge places only limited restrictions on the range of possibilities-nonparametric and seimparametric estimation problems. Bayesian methods are attractive because they have risk optimality properties, and because they can be used to generate predictive distributions for future outcomes that incorporate parameter uncertainty. Previous work by the investigator on semiparametric Bayesian inference in models for longitudinal data will be extended to allow for additional regressors, binary outcomes, and alternative representations of unknown disturbance densities

Many policy questions can be framed as questions about treatment effects defined in terms of potential outcomes. The second component of this project will apply flexible Bayesian methods to estimation problems involving treatment effects, and compare this approach to more conventional methods for learning about these treatment effects. One part of' this research will consider inference for treatment effects with continuous treatments when treatment assignment is independent of potential outcomes conditional on a vector of pretreatment variables. Another aspect of the research is to extend earlier research on parametric Bayesian approaches to causal instrumental variables estimation to semiparametric models.

The third component of this project focuses on bringing selected recent developments in econometric methodology into the classroom in order to better train graduate students in practical, flexible empirical methods.