YE LUO
lkurt@mit.edu
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
OFFICE CONTACT INFORMATION
MIT Department of Economics
77 Massachusetts Avenue, E19-750
Cambridge, MA 02139
(860) 933-6236
lkurt@mit.edu
HOME CONTACT INFORMATION
550 Memorial Drive, apt. 2D-2
Cambridge, MA 02139
Mobile: (860) 933-6236
http://economics.mit.edu/grad/lkurt
MIT PLACEMENT OFFICER
Professor Benjamin Olken
(617) 253-6833
DOCTORAL
STUDIES
bolken@mit.edu
MIT PLACEMENT ADMINISTRATOR
Ms. Beata Shuster
bshuster@mit.edu
(617) 324-5857
Massachusetts Institute of Technology (MIT)
PhD, Economics, Expected completion June 2015
Dissertation:“Essays in High Dimensional Econometrics and Model Selection”
Dissertation Committee and References
Professor Victor Chernozhukov
MIT Department of Economics
77 Massachusetts Avenue, E17-242
Cambridge, MA 02139
(617) 253-4767
vchern@mit.edu
Professor Jerry Hausman
MIT Department of Economics
77 Massachusetts Avenue, E17-238A
Cambridge, MA 02139
(617) 253-3644
jhausman@mit.edu
Professor Whitney Newey
MIT Department of Economics
77 Massachusetts Avenue, E19-760
Cambridge, MA 02139
(617) 253-6420
wnewey@mit.edu
Professor Anna Mikusheva
MIT Department of Economics
77 Massachusetts Avenue, E17-210
Cambridge, MA 02139
(617) 253-5459
amikushe@mit.edu
PRIOR
EDUCATION
Massachusetts Institute of Technology
B.S. in Mathematics, B.S. in Economics
Peking University
Major in Mathematics
CITIZENSHIP
P.R. of China
LANGUAGES
Chinese, English
GENDER:
2007-2010
2005-2007
Male
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OCTOBER 2014 -- PAGE 2
FIELDS
Primary Field: Econometrics
Secondary Fields: Finance, Industrial Organization
TEACHING
EXPERIENCE
Statistical Method in Economics (PhD, MIT 14.381)
Teaching Assistant to Professor Anna Mikusheva and
Visiting Professor Alfred Galichon (2014)
Teaching Assistant to Professor Anna Mikusheva and
Visiting Professor Kirill Evodokimov (2013)
Non-Linear Econometric Analysis (PhD, MIT 14.385)
Teaching Assistant to Professor Victor Chernozhukov and
Professor Whitney Newey
Econometrics (PhD, MIT 14.382)
Teaching Assistant to Professor Jerry Hausman
Econometrics (Undergraduate, MIT 14.32)
Teaching Assistant to Visiting Professor Yingying Dong
Principles of Microeconomics (Undergraduate, MIT 14.01)
Lecturer, Teaching Assistant to Professor Jeffery Harris
RELEVANT
POSITIONS
Research Assistant to Professor Jerry Hausman
Research Assistant to Professor Victor Chernozhukov
FELLOWSHIPS, MIT Economics Department Fellowship
HONORS, AND Mingde Fellowship, Peking University
AWARDS
Gold Medal and 1st rank with perfect score,
45th International Mathematical Olympiad
Gold Medal and 1st rank with perfect score,
20th Chinese Mathematical Olympiad
Fall 2014 &
Fall 2013
Fall 2014 &
Fall 2012
Spring 2014
Fall 2013
Spring 2013
2012-Present
2009-Present
2010-2012
2005-2007
2005
2005
PROFESSIONAL Referee for Bernoulli, Econometric Theory
ACTIVITIES
RESEARCH
PAPERS
“Selecting Informative Moments via LASSO” (Job Market Paper)
The traditional optimal GMM estimator is biased when there are many moments.
This paper considers the case when information is sparse, i.e., only a small set of
the moment conditions is informative. We propose a LASSO based selection
procedure in order to choose the informative moments and then, using the
selected moments, conduct optimal GMM. Our method can significantly reduce
any second-order bias of the optimal GMM estimator while retaining most of the
information in the full set of moments. The LASSO selection procedure allows
the number of moments to be much larger than the sample size. We establish
bounds and asymptotics of the LASSO and post-LASSO estimators. Under our
approximate sparsity condition, both estimators are asymptotically normal and
nearly efficient when the number of strongly informative moments grows at a
slow rate compared to the sample size. The formulation of LASSO is a convex
optimization problem and thus the computational cost is low compared to other
moment selection procedures such as those described in Donald, Imbens and
YE LUO
OCTOBER 2014 -- PAGE 3
RESEARCH
PAPERS
Newey (2008). In an IV setting with homoskedastic errors, our procedure
reduces to the IV-LASSO method stated in Belloni, Chen, Chernozhukov and
Hansen (2012). We propose penalty terms using data-driven methods. Since the
construction of the penalties requires the knowledge of unknown parameters, we
propose adaptive algorithms which perform well globally. These algorithms can
be applied to more general L1-penalization problems when the penalties depend
on the parameter to be estimated. In simulation experiments, the LASSO-based
GMM estimator performs well when compared to the optimal GMM estimator
and CUE.
“Summarizing Partial Effects Beyond Average”
With Victor Chernozhukov
For partial effects under consideration, the usual modus operandi is to report the
average partial effects. In this paper, instead of the average, we consider sorting
the effects into an increasing function. We derived the asymptotic properties of
the sorted curve of partial effects given any probability measure. In particular,
we proved the asymptotic properties of the rearranged curve for the empirical
measure, thereby providing inferences for each quantile of the partial effect with
i.i.d data. Though the analytical confidence interval is often difficult to compute,
we suggest a bootstrap method which proves to be valid for inference. The
technique we develop in this paper allows us to demonstrate the entire image of
the partial effects rather than simply the average as is usual in econometrics. We
present Monte-Carlo examples to show how the procedure works. We apply our
method to study the returns-to-education problem using 1980 PUMS data.
“Shape Regularization In Econometrics”
With Victor Chernozhukov and Ivan Fernandez-Val
In many economic applications one encounters functions with certain shapes.
We study a group of functional operators including Legendre Transformation,
rearrangement and projection, which enforce certain shapes such as convexity
and monotonicity on functions. Suppose there is an estimator of the true
function. If the true function has certain shape properties, by applying the
corresponding functional operators to the estimator, one can construct a function
of the desired shape that is closer to the true function under some distance. We
show that the inference can be done similarly by applying the corresponding
operators on the boundary of the uniform confidence bands of the true function
(with slight modifications). We provide conditions for such a simple inference
strategy to be valid.
“EIV on Dependent Variable of Quantile Regression Models”
With Jerry Hausman and Christopher Palmer
The usual quantile regression estimator of Koenker and Bassett (1978) is biased
if there is an additive error term in the dependent variable. We analyze this
problem as an errors-in-variables problem where the dependent variable suffers
from the classical measurement error and develop a sieve maximum-likelihood
approach that is robust to left-hand side measurement error. After describing
sufficient conditions for identification, we employ global optimization
algorithms (specifically, a genetic algorithm) to solve the MLE optimization
problem. We also propose a computationally tractable EM-type algorithm that
YE LUO
OCTOBER 2014 -- PAGE 4
RESEARCH
PAPERS
accelerates the computational speed in the neighborhood of the optimum. When
the number of knots in the quantile grid is chosen to grow at an adequate speed,
the sieve maximum-likelihood estimator is asymptotically normal. We verify our
theoretical results with Monte Carlo simulations and illustrate our estimator with
an application to the returns to education highlighting important changes over
time in the returns to education that have been obscured in previous work by
measurement-error bias.
“Core Determining Class: Construction, Approximation and Inference”
With Hai Wang
The relations between unobserved events and observed outcomes in partially
identified models can be characterized by a bipartite graph. We estimate the
probability measure on the events given observations of the outcomes based on
the graph. The feasible set of the probability measure on the events is defined by a
set of linear inequality constraints. The number of inequalities is often much
larger than the number of observations. The set of irredundant inequalities is
known as the Core Determining Class. We propose an algorithm that explores the
structure of the graph to construct the exact Core Determining Class when data
noise is not taken into consideration. We prove that if the graph and the measure
on the observed outcomes are non-degenerate, the Core Determining Class does
not depend on the probability measure of the outcomes but only on the structure
of the graph. For more general problem of selecting linear inequalities under
noise, we investigate the sparse assumptions on the full set of inequalities, i.e.,
only a few inequalities are truly binding. We show that the sparse assumptions are
equivalent to certain sparse conditions on the dual problems. We propose a
procedure similar to the Dantzig Selector to select the truly informative
constraints. We analyze the properties of the procedure and show that the feasible
set defined by the selected constraints is a nearly sharp estimator of the true
feasible set. Under our sparse assumptions, we prove that such a procedure can
significantly reduce the number of inequalities without discarding too much
information. We apply the procedure to the Core Determining Class problem and
obtain a stronger theorem taking advantage of the structure of the bipartite graph.
We design Monte-Carlo experiments to demonstrate the good performance of our
selection procedure, while the traditional CHT inference is difficult to apply.
“Semi-Parametric Partially Linear Regression with Interval Data”
With Victor Chernozhukov and Denis Chetverikov
Conditional moment inequalities induce infinitely many inequality constraints on
the parameter of interest. One important case is that the observed dependent
variables are intervals in the linear regression model, which is often referred as
interval regression. We discover a new geometric method of depicting the
identification region for interval regression and semi-parametric partially linear
models. Using the Legendre Transformation, we establish a simple duality
between the sharp identification region of the parameter and the convex hulls of
the conditional mean functions of the upper and lower bounds of the dependent
variable. The order-preserving property of the Legendre transformation provides
an additional duality between the confidence set of the parameter and the
confidence set of the convex hull of the conditional mean functions of the upper
and lower bounds of the dependent variable. The inference procedure is
YE LUO
OCTOBER 2014 -- PAGE 5
developed based on traditional non-parametric uniform inference. The procedure
is computationally tractable and easy to implement. We propose a similar
procedure to estimate and perform inference of the identified region when the
conditional moment inequalities are semi-parametric and partially linear in the
parameter of interest. We demonstrate the performance of our procedure in
Monte-Carlo experiments.
RESEARCH IN
PROGRESS
“Functional Inequalities: Optimal Discretization, Estimation and Inference”
With Hai Wang
This paper extends the Core-Determining-Sets problem to the situation with
continuous correspondence mapping instead of discrete mapping. Our setting
allows partially-identified models to be estimated non-parametrically under
certain smooth assumptions. We discuss optimal discretization of the
correspondence mapping and derive the optimal number of cells being
discretized. Our method adaptively adjusts this number in order to balance the
approximation error and the estimation error given a specific model and
observed data.
“Adaptive Non-Parametric Smoothing on Discrete Variables”
With Martin Spindler
Non-parametric smoothing proposed in Li and Racine (2001) may substantially
improve the performance of non-parametric methods such as kernel estimation
when there exist discrete variables. While Li and Racine (2001) makes
smoothing weights equally the same across different values of the discrete
variables, we develop a method using data-driven weights based on test statistics
proposed in Hardle and Mammen (1993) and Dette and Neumeyer (2003).
Preliminary results show that our procedure performs better than Li and Racine
(2001) and traditional non-parametric methods, especially when the sample size
is relatively small.