Yulong Wang

Ph.D. Candidate in Economics
Princeton University

I am on the job market and will be available for interviews
at the ASSA meeting in Chicago from Jan. 5th to Jan. 8th.

Contact Information
Department of Economics
Fisher 100
Princeton University
08544 Princeton, NJ
Phone: +1 (310) 999 5122
Email: yulong@princeton.edu

Personal Information
CV (pdf)

Research Interests
Primary: Econometrics, Applied Econometrics
Secondary: Risk Management, Finance

Forthcoming and Published Papers

      .  Accepted for publication at the Journal of the American Statistical Association. (Joint with Ulrich K. Müller.)    Supplementary Matlab code.

Working Papers

     

This paper studies inference about the values of the parameters in the threshold model in a generalized method of moments (GMM) framework. First, we establish that the extensively studied least squares method leads to substantially oversized tests and confidence intervals when the coefficient change is not large. Second, by re-ordering the data to recast the threshold model as a structural break problem, we construct tests that control size under a large range of empirically relevant moderate coefficient changes and are approximately efficient in a well-defined sense. Finally, we modify our approach to encompass inference problems in a variety of additional widely studied models. The accuracy of the asymptotic approximations is evaluated by Monte Carlo simulations. The empirical applicability is illustrated through the tipping point problem studied by Card, Mas, and Rothstein (2008). We find a substantially different confidence interval of the tipping point from the one obtained by the least squares method, which might deliver a coverage that is much lower than the nominal level. 

       .  (Joint with Ulrich K. Müller.)    Supplementary Matlab code.

We study non-standard parametric estimation problems, such as the estimation of the AR(1) coefficient close to the unit root. We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We demonstrate the usefulness of our generic approach by also applying it to estimation in a predictive regression, estimation of the degree of time variation, and long-range quantile point forecasts for an AR(1) process with coefficient close to unity.  


Working Papers in Progress

       Testing Structural Change in Tail Properties. 

       Unbiased Estimation of Value-at-Risk and Expected Shortfall.  (Joint with Ulrich K. Müller.)