Theses Doctoral

Estimating Structural Models with Bayesian Econometrics

Sacher, Szymon Konrad

With the ever-increasing availability of large, high-dimensional datasets, there is a growingneed for econometric methods that can handle such data. The last decade has seen the development of many such methods in computer science, but their applications to economic models have been limited. In this thesis, I investigate whether modern tools in (exact and approximate) Bayesian inference can be useful in economics. In the three chapters, my coauthors and I develop and estimate a variety of models applied to problems in organizational economics, health, and labor. In chapter one, joint with Andrew Olenski, we estimate a mortality-based Bayesian model of nursing home quality accounting for selection. We then conduct three exercises. First, we examine the correlates of quality, and find that public report cards have near-zero correlation. Second, we show that higher quality nursing homes fared better during the pandemic: a one standard deviation increase in quality corresponds to 2.5% fewer Covid-19 cases. Finally, we show that a 10% increase in the Medicaid reimbursement rate raises quality, leading to a 1.85 percentage point increase in 90-day survival. Such a reform would be cost-effective under conservative estimates of the quality-adjusted statistical value of life.

In chapter two, joint with Laura Battaglia and Stephen Hansen, we demonstrate the effectiveness of Hamiltonian Monte Carlo (HMC) in analyzing high-dimensional data in a computationally efficient and methodologically sound manner. We propose a new model, called Supervised Topic Model with Covariates, that shows how modeling this type of data carefully can have significant implications on conclusions compared to a simpler yet methodologically problematic two-step approach. By conducting a simulation study and revisiting the study of executive time use by Bandiera, Prat, Hansen, and Sadun (2020), we demonstrate these results. This approach can accommodate thousands of parameters and doesn’t require custom algorithms specific to each model, making it more accessible for applied researchers.

In chapter three, I propose a new way to estimate a two-way fixed effects model such as Abowd, Kramarz, and Margolis (1999) (AKM) that relaxes the stringent assumptions concerning the matching process. Through simulations, I demonstrate that this model performs well and provide an application to matched employer-employee data from Brazil. The results indicate that disregarding selection may result in a significant bias in the estimates of location fixed effects, and thus, can contribute to explaining recent discoveries about the relevance of locations in US labor markets.

The three chapters demonstrate the usefulness of modern Bayesian methods for estimating models that would be otherwise infeasible, while remaining accessible enough for applied researchers. The importance of carefully modeling the data of interest instead of relying on ad-hoc solutions is also highlighted, as it has been shown to significantly impact the conclusions drawn across a variety of problems.

Geographic Areas


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More About This Work

Academic Units
Thesis Advisors
Prat, Andrea
Ph.D., Columbia University
Published Here
April 19, 2023