“Estimation and Inference with a (Nearly) Singular Jacobian”* with Adam McCloskey (Latest Version: June 18, 2016. Submitted)
“Nonparametric Estimation of Triangular Simultaneous Equations Models under Weak Identification” (Latest Version: February 19, 2017. Resubmitted, The Journal of Econometrics)
- Matlab Codes Download: ZIP file (Latest Version: March 2014.)
This paper analyzes the problem of weak instruments on identification, estimation, and inference in a simple nonparametric model of a triangular system. The paper derives a necessary and sufficient rank condition for identification, based on which weak identification is established. Then nonparametric weak instruments are defined as a sequence of reduced form functions where the associated rank shrinks to zero. The problem of weak instruments is characterized to be similar to the ill-posed inverse problem, which motivates the introduction of a regularization scheme. The paper proposes a penalized series estimation method to alleviate the effects of weak instruments. The rate of convergence of the resulting estimator is given, and it is shown that weak instruments slow down the rate and penalization derives a faster rate. Consistency and asymptotic normality results are also derived. Monte Carlo results are presented, and an empirical example is given, where the effect of class size on test scores is estimated nonparametrically.
“Identification in a Generalization of Bivariate Probit Models with Dummy Endogenous Regressors” with Edward Vytlacil (Latest Version: December 10, 2016. Resubmitted, The Journal of Econometrics)
“CQIV: Stata Module to Perform Censored Quantile Instrumental Variable Regression” with Victor Chernozhukov, Ivan Fernandez-Val, and Amanda Kowalski (Latest Version: June 2012.)
“How Would Information Disclosure Influence Organizations' Outbound Spam Volume? Evidence from a Field Experiment” with Shu He, Gene Moo Lee, and Andy Whinston (Latest Version: July 1, 2016. Forthcoming, Journal of Cybersecurity)
Cyber-insecurity is a serious threat in the digital world. In the present paper, we argue that a suboptimal cybersecurity environment is partly due to organizations’ underinvestment on security and a lack of suitable policies. The motivation for this paper stems from a related policy question: how to design policies for governments and other organizations that can ensure a sufficient level of cybersecurity. We address the question by exploring a policy devised to alleviate information asymmetry and to achieve transparency in cybersecurity information sharing practice. We propose a cybersecurity evaluation agency along with regulations on information disclosure. To empirically evaluate the effectiveness of such an institution, we conduct a large-scale randomized field experiment on 7919 US organizations. Specifically, we generate organizations’ security reports based on their outbound spam relative to the industry peers, then share the reports with the subjects in either private or public ways. Using models for heterogeneous treatment effects and machine learning techniques, we find evidence from this experiment that the security information sharing combined with publicity treatment has significant effects on spam reduction for original large spammers. Moreover, significant peer effects are observed among industry peers after the experiment.
Work in Progress:
“Sensitivity Analysis in Triangular Systems of Equations with Binary Endogenous Variables” with Sungwon Lee
“Invalidity of the Bootstrap and the m out of n Bootstrap for Confidence Interval Endpoints Defined by Moment Inequalities” with Donald Andrews, Econometrics Journal (2009), Volume 12, pp. S172–S199.
This paper analyses the finite-sample and asymptotic properties of several bootstrap and m out of n bootstrap methods for constructing confidence interval (CI) endpoints in models defined by moment inequalities. In particular, we consider using these methods directly to construct CI endpoints. By considering two very simple models, the paper shows that neither the bootstrap nor the m out of n bootstrap is valid in finite samples or in a uniform asymptotic sense in general when applied directly to construct CI endpoints.