This paper considers parametric/semiparametric estimation and inference in a class of bivariate threshold crossing models with dummy endogenous variables. We investigate the consequences of common practices made by empirical researchers using this class of models, such as the parametric specification of the joint distribution of the unobservables. To address the problem of misspecification, we propose a semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root-n normality, for the sieve maximum likelihood estimators that can be used for inference on the individual structural parameters and the average treatment effects. Numerical studies suggest the sensitivity of parametric specification and the robustness of semiparametric estimation. This paper also shows that the absence of excluded instruments results in the failure of identification, unlike what some practitioners believe. We apply our theoretical findings to conduct a sensitivity analysis and make robust assessment of the effects of attending Catholic schools on high school completion.
- Matlab Codes Download: ZIP file (Latest Version: February 2017.) 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. “CQIV: Stata Module to Perform Censored Quantile Instrumental Variable Regression” with Victor Chernozhukov, Ivan Fernandez-Val, and Amanda Kowalski (Latest Version: June 2012.)
“Identification in a Generalization of Bivariate Probit Models with Dummy Endogenous Regressors” with Edward Vytlacil, The Journal of Econometrics (2017), Volume 199, pp. 63-73.
“How Would Information Disclosure Influence Organizations' Outbound Spam Volume? Evidence from a Field Experiment” with Shu He, Gene Moo Lee, and Andy Whinston, Journal of Cybersecurity (2016), Volume 2, pp. 99-118.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.
This paper analyses the finite-sample and asymptotic properties of several bootstrap and |