Working Papers
Job Market Paper
This paper establishes statistical properties of deep neural network (DNN) estimators under dependent data. Two general results for nonparametric sieve estimators directly applicable to DNN estimators are given. The first establishes rates for convergence in probability under nonstationary data. The second provides non-asymptotic probability bounds on L2 -errors under stationary β-mixing data. I apply these results to DNN estimators in both regression and classification contexts imposing only a standard Hölder smoothness assumption. The DNN architectures considered are common in applications, featuring fully connected feedforward networks with any continuous piecewise linear activation function, unbounded weights, and a width and depth that grows with sample size. The framework provided also offers potential for research into other DNN architectures and time-series applications.
I consider inference in a partially linear regression model under stationary β-mixing data after first stage deep neural network (DNN) estimation. Using the DNN results of Brown (2024), I show that the estimator for the finite dimensional parameter, constructed using DNN-estimated nuisance components, achieves √n-consistency and asymptotic normality. By avoiding sample splitting, I address one of the key challenges in applying machine learning techniques to econometric models with dependent data. In a future version of this work, I plan to extend these results to obtain general conditions for semiparametric inference after DNN estimation of nuisance components, which will allow for considerations such as more efficient estimation procedures, and instrumental variable settings.
Publications
Forum Shopping and Legal Labor Markets: Evidence from the Court Competition Era (2024), with Jeronimo Carballo and Alessandro Peri, Journal of Law and Economics
Focusing on Chapter 11 bankruptcy reorganizations of publicly listed firms during the court competition era (1991–96), we document local legal employment effects of forum shopping, a stipulation of the law that allows firms to file for bankruptcy far from their headquarters. Bankruptcy shocks increase legal-sector employment in the bankrupt firm’s locale, but forum shopping nullifies this effect. Employment gains of received forum-shopped cases are concentrated in Delaware, with no effect in other receiving forums. Quantification shows that Delaware handled these forum-shopped bankruptcies with just one-fifth of the additional legal workforce that would have been needed if the cases were handled in the firms’ locales. This increase in productivity also coincides with substantial missed potential employment gains in communities where bankruptcies were diverted through forum shopping. The analysis uncovers meaningful effects of forum shopping on local legal labor markets, so far overlooked in the policy debate.
Previously circulated as: "Bankruptcy Shocks and Legal Labor Markets: Evidence from the Court Competition Era".
Work in Progress
Uniform Convergence of Deep Neural Network Sieve Estimators
Inference after Machine Learning under Dependent Data
Value Function Estimation with Deep Neural Networks
A Structural Model of Legal Sector Labor Markets
A detailed summary of my research agenda can be found here: Research Statement (Dec. 20, 2024).