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I am a doctoral candidate in economics at the University of Missouri. My research interest is Econometrics (Theoretical & Applied). I will be on the job market starting Fall 2023.

- Ph.D. Economics, University of Missouri (advisor: David M. Kaplan), (expected) 2024
- M.S. Economics, Qingdao University, 2019
- B.A. Economics, Qingdao University, 2016

- Econometrics (Theoretical and Applied)

**Comparing latent inequality with ordinal data (with David M. Kaplan),***The Econometrics Journal***Financial Structure, Technological Innovation and Economic Growth in Shandong Province——Based on SDM Analysis (with Jianjun Zhao)**,**Journal of Financial and Economic Theory**

2023

2018

Instead of having a “yes” or “no” result from a test of the global null hypothesis that a function is increasing, I propose a multiple testing procedure to test at multiple points. If the global null is rejected, then this multiple testing provides more information about why. If the global null is not rejected, then multiple testing can provide stronger evidence in favor of increasingness, by rejecting the null hypotheses that the function is decreasing. With high-level assumptions that apply to a wide array of models, this approach can be used to test for monotonicity of a function in a broad class of structural and descriptive econometric models. By inverting the proposed multiple testing procedure that controls the familywise error rate, I also equivalently generate “inner” and “outer” confidence sets for the set of points at which the function is increasing. With high asymptotic probability, the inner confidence set is contained within the true set, whereas the outer confidence set contains the true set. I also improve power with stepdown and two-stage procedures. Simulated and empirical examples (income–education conditional mean, and IV Engel curve) illustrate the methodology.

- **Conditions for Extrapolating Differences in Consumption to Differences in Welfare (with David M. Kaplan) (R&R resubmitted at
*Economic Inquiry*)**

We characterize conditions under which a better consumption distribution implies higher utility. Specifically, when comparing two populations, we consider when one population’s first-order stochastic dominance in consumption implies higher expected utility for each subpopulation of individuals who have the same utility function, compared to the corresponding subpopulation of the lower-consumption population. Although this implication seems natural and indeed holds in the familiar case where everyone has the same utility function (risk preferences), we first provide an example in which the opposite occurs: despite worse consumption, expected utility is higher in every subpopulation, essentially by trading consumption risk between subpopulations in ways that are Pareto-improving. We then show that higher expected utility results from higher consumption in different settings. First, we assume a fixed dependence structure (copula) between consumption and preferences, with independence as a special case. Second, viewing the two distributions as treated and untreated potential outcomes, we use the rank invariance assumption from the treatment effects literature, without any explicit restrictions on the consumption–preferences dependence structure. Given that empirical studies only learn about consumption differences, our results help make explicit when such differences can be interpreted as individuals being better off.

**Heckman sample selection estimators under heteroskedasticity (with Alyssa Carlson) (Ready to submit)**

This paper studies the properties of two Heckman sample selection estimators, full information maximum likelihood (FIML) and limited information maximum likelihood (LIML), under heteroskedasticity. In this case, FIML is inconsistent while LIML can be consistent in certain settings. For the LIML estimator, we provide robust asymptotic variance formulas, not currently provided with standard Stata commands. Since heteroskedasticity affects these two estimators’ performance, this paper also offers guidance on how to properly test for heteroskedasticity. We propose a new demeaned Breusch–Pagan test to detect general heteroskedasticity in sample selection settings as well as a test for when LIML is consistent under heteroskedasticity. The Monte Carlo simulations illustrate that both of the proposed test procedures perform well.