Research

Working Papers

Identification of a triangular random coefficient model using a correction function (Accepted at Review of Economics and Statistics)

Previously, identification of triangular random coefficient models required a restriction on the dimension of the first stage heterogeneity or independence assumptions across the different sources of the heterogeneity. This note proposes a new identification strategy that does not rely on either of these restrictions but rather uses conditional linear projections to construct “correction functions” to address endogeneity and gain identification of the average partial effect. This identification strategy allows for both continuous and discrete instruments although the formulation of the correction function differs depending on the setting. Finally, a simple simulation illustrates that the proposed identification strategy is valid in settings where no other existing methods can identify average partial effects.

Addressing Attrition in Nonlinear Dynamic Panel Data Models with an Application to Health (Joint with Anastasia Semykina)

We present a general framework for nonlinear dynamic panel data models subject to missing outcomes due to endogenous attrition. We consider two cases of attrition. First, ignorable attrition where the distribution of the outcome does not depend on missingness conditional on the unobserved heterogeneity. Second, non-ignorable attrition where the conditional distribution of the outcome does depend on attrition. In either case, a major challenge posed by the dynamic specification is the inherent correlation between the lagged dependent variable and unobserved individual heterogeneity. Our key assumption is that the distribution of the unobserved heterogeneity does not depend on attrition conditional on observed covariates and initial condition. The resulting estimator is a joint MLE that accommodates a dynamic specification, correlated unobserved heterogeneity, and endogenous attrition. We discuss the derivation and estimation of the average partial effects within this framework and provide examples for the binary response, ordinal response, and corner solution cases. Finite sample properties are studied using Monte Carlo simulations. As an empirical application, the proposed method is applied to estimating a dynamic health model for older women. 

Heterogeneity in State Solar and Wind Deployments: Trade-offs between Technology-neutral and Technology-specific Renewable Energy Policies (Joint with Jian Chen and Hongli Feng)

There are large heterogeneities among U.S. states regarding solar and wind energy development and natural endowment. We examine the roles of renewable portfolio standards (RPS) without or with solar carve-outs (SRPS) in the uneven solar and wind development across states. We first develop a theoretical framework that differentiates between renewables and nonrenewables and between two types of renewables (solar and wind) to evaluate the impacts of technology-neutral or specific renewable energy policies. Our results with state-level data from 2001 to 2019 suggest that adopting a solar carve-out within RPS boosts solar share by 0.15 percentage points but decreases wind share by 0.46 percentage points. Our results also indicate that the decreasing cost of solar and wind electricity during our study period contributed to solar and wind development by 0.65 and 5.42 percentage points, respectively. We also examine the role of credit trading, costs of SRPS, heterogeneous impacts across time periods, operator scales, and treatment timing.

Publications

Heckman sample selection estimators under heteroskedasticity. with Wei Zhao. Stata Journal (2025)

gtsheckman: Generalized Two Step Heckman Estimator. Stata Journal (2024)

(SSC on RePEc) (Stata 2022 Conference Slides)

Addressing Sample Selection Bias for Machine Learning Methods. with Dylan Brewer. Journal of Applied Econometrics (2024)

(Supplementary Appendix) (Accepted version)

Sample Selection in Linear Panel Data Models with Heterogeneous Coefficients. with Riju Joshi. Journal of Applied Econometrics (2023) 

(Supplementary Appendix) (Accepted version)

Relaxing Conditional Independence in an Endogenous Binary Response Model. Journal of Econometrics (2023)

(Supplementary Appendix) (Accepted version)

Parametric Identification of Multiplicative Exponential Heteroskedasticity​. Oxford Bulletin of Economics and Statistics (2018)

(Accepted version)

Grants

2024-2029 NSF CAREER Grant: Heterogeneity and the General Control Function Approach to Endogeneity ($400,000) 

Resting Papers

Behavior of Pooled and Joint Estimators in Probit Model with Random Coefficients and Serial Correlation. (Joint with Jeffrey Wooldridge and Ying Zhu)