YING-YING LEE
October 2012
Department of Economics
University of Wisconsin-Madison
1180 Observatory Drive
Madison, WI 53706-1393
Office: (608) 262-0200
Mobile: (608) 698-9608
ylee55@wisc.edu
https://mywebspace.wisc.edu/ylee55/web/
Citizenship: Taiwan (F-1 Visa)
Education:
Ph.D., Economics, University of Wisconsin-Madison
M.S., Economics, University of Wisconsin-Madison
M.B.A., Financial Engineering, National Taiwan University
B.S. Physics, National Taiwan University
Expected 2013
Dec 2009
Jun 2006
Jun 2004
Fields of Interest: Econometric Theory, Applied Econometrics
Desired Teaching: Econometrics and Applied Econometrics, Probability and Statistics
Working Papers:
“Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable
Models” (Job Market Paper), 2012
“Nonparametric Density-Weighted Average Quantile Derivative, ” 2011
“Semiparametric Efficiency in Quantile Regression under Misspecification,” 2010
Teaching Experience:
Teaching Assistant, Economic Statistics and Econometrics II (Graduate), UW-Madison
Spring 2011
Teaching Assistant, Economic Statistics and Econometrics I (Graduate), UW-Madison
Fall 2010
Teaching Assistant, Introductory Econometrics, UW-Madison
Fall 2009
Teaching Assistant, Principles of Microeconomics, UW-Madison
Fall 2008 & Spring 2009
Research Experience:
Project Assistant for Jack Porter, UW-Madison
Project Assistant for Bruce E. Hansen, UW-Madison
Research Assistant for Chung-Ming Kuan, Academia Sinica
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Fall 2011 - Fall 2013
Spring 2010
Fall 2006 - Spring 2007
Awards:
Harold Groves Paper Prize: Quantile Regression and the U.S. Wage Structure,”
Econ 880- UW-Madison.
Dr. Roger B. Andreae Scholarship Fund, UW-Madison
College Tuition Fellowship with Exemption from Entrance Examination,
National Science Council, Taiwan
Conference Presentation:
Info-Metrics Nonparametric Conference
The 22th Annual Meetings of the Midwest Econometrics Group
The 21th Annual Meetings of the Midwest Econometrics Group
The 20th Annual Meetings of the Midwest Econometrics Group
2008
2007
2000
Nov 2012
Sept 2012
Oct 2011
Oct 2010
Referee for Economics Journals: Journal of Econometrics, Journal of Business and Economic Statistics, Journal of Econometric Methods
Skill: R, Matlab, C, Stata
Languages: English (fluent), Chinese (native)
References:
Jack R. Porter (Chair)
Department of Economics
University of Wisconsin-Madison
1180 Observatory Drive
Madison, WI 53706-1393
Phone: (608) 263-3870
Email: jrporter@ssc.wisc.edu
Bruce E. Hansen
Department of Economics
University of Wisconsin-Madison
1180 Observatory Drive
Madison, WI 53706-1393
Phone: (608) 263-3880
Email: behansen@wisc.edu
Xiaoxia Shi
Department of Economics
University of Wisconsin-Madison
1180 Observatory Drive
Madison, WI 53706-1393
Phone: (608) 262-8910
Email: xshi@ssc.wisc.edu
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Dissertation Abstract:
In Chapter 1, the unconditional distributional features of potential outcomes with a continuous treatment
and the quantile structural function in a nonseparable triangular model can both be expressed as functions
of a partial mean process. I propose a multi-step nonparametric kernel-based estimator for this partial mean
process with generated regressors. A uniform expansion reveals the influence of estimating the generated
regressors on the final estimator. Chapter 2 proposes a semiparametric average quantile derivative estimator which is robust to misspecificaton and converges at the parametric rate. Chapter 3 studies the efficiency
bound for the classical quantile regression and the parallel properties with the least squares projection model
when the linear conditional quantile functions are misspecified.
I. Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models The unconditional distribution of the potential outcome with a continuous treatment can be
expressed as a partial mean process. The inverse function of the partial mean process defines the quantile
structural function in a nonseparable triangular model. I propose a multi-step nonparametric kernel-based
estimator for this partial mean process with generated regressors. A uniform expansion reveals the influence
of estimating the generated regressors on the final estimator. In the case of continuous treatment effects,
an unconfoundedness assumption leads to regression on (or weighting by) the generalized propensity score
(Hirano-Imbens 2004), which serves as the generated regressor in the partial mean process. Analogous to
binary treatment effect case, my results suggest the generalized propensity score reduces the dimension of
nonparametric regression in estimation, but does not improve first-order asymptotic efficiency. In nonseparable models, a conditional independence assumption is commonly considered that yields a control function
approach to deal with endogeneity (Imbens-Newey 2009, Altonji-Matzkin 2005). The control function variable serves as the generated regressor in this setup. By extending my results to Hadamard-differentiable
functions of the partial mean process, I am able to provide the limit distribution for estimation of common
inequality measures and various distributional features of the outcome variable, such as the quantile response
function. Monte Carlo results demonstrate the finite sample behavior of my estimator. Additionally, a substantive empirical application using data from a Colombian conditional cash transfer program illustrates the
usefulness of my findings for continuous treatment effect estimation.
II. Nonparametric Density-Weighted Average Quantile Derivative I estimate the density-weighted Average Quantile Derivative (AQD), defined as the expectation of the partial derivative of the conditional quantile
function (CQF) weighted by the density function of the covariates. The proposed estimator achieves rootn-consistency and asymptotic normality by a first-step nonparametric kernel estimation for the unknown
functions and a second-step sample analogue of a full-mean. Therefore, the AQD summarizes the average
marginal response of the covariates on the CQF and can be viewed as a nonparametric quantile regression
coefficient. Similar to the widely studied average derivative in mean regression, the AQD identifies the coefficients up to scale in semiparametric single-index models. For the nonparametric nonseparable structural
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model, the derivative of the CQF identifies the structural derivative, under the conditional independence
assumption in Hoderlein and Mammen (2007).
III. Semiparametric Efficiency in Quantile Regression under Misspecification Allowing for misspecification in the linear conditional quantile function (CQF), I calculate the semiparametric efficiency bound for
the quantile regression (QR) parameter, the best linear predictor for a response variable under the asymmetric
check loss function. The crossing problem implies the linear QR model is inherently misspecified. Angrist,
Chernozhukov and Fernández-Val (2006) interpret the QR parameter as the best weighted mean-squared
linear approximation to the true CQF. My results suggest that the sample analog estimator by Koenker
and Bassett (1978) semiparametrically efficiently estimates a pseudo-true parameter that offers meaningful
descriptive statistics for the conditional distribution.
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