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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.


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Working Papers

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.

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.

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