Assistant Professor of Economics, UCLA




Field of Concentration: 
Econometric Theory and Applied
Econometrics 


Research Interest: 
Model Selection and Model Averaging, Inference of Semi/Nonparametric Models, Modelling and Inference of Nonstationary Time Series 


Office: 
8379 Bunche Hall, UCLA 


Email: 


Phone: 
(310) 7945427 


Fax: 
(310) 8259528 


Mailing Address: 
Department of Economics Los Angeles, CA 90095 



Working Papers



Published and Forthcoming Papers
8. "Nonparametric TwoStep Sieve M
Estimation and Inferences", (with Jinyong Hahn and
Geert Ridder), Econometric Theory,
forthcoming, 2018. [Supplemental Appendix] In this paper, we study twostep sieve M
estimation of general semi/nonparametric models, where the second step
involves sieve estimation of unknown functions that may use the nonparametric
estimates from the first step as inputs, and the parameters of interest are
functionals of unknown functions estimated in both steps. We establish the
asymptotic normality of the plugin twostep sieve M estimate of a functional
that could be rootn estimable. The asymptotic variance of the sieve M
estimate may not have closed form expression, but can be approximated by a
sieve variance that characterizes the effect of the firststep estimation on
the secondstep estimate. We provide a simple consistent estimate of the
sieve variance, thereby facilitating Wald type inferences based on the
Gaussian approximation. 
9. "On Standard Inference for GMM with Local Identification Failure
of Known Forms", (with
Ji Hyung Lee), Econometric Theory, Vol.34, 2018, pp. 790814.
[Supplemental Appendix] Given the validity of the moment
conditions, their identification strength, measured by the information they
contain about the unknown parameters, plays the key role of determining the
properties of the GMM estimator. The GMM estimator may be inconsistent or
converge to the true value slowly as the sample size increases if the
identification strength is weak. One interesting example arises in testing
the existence of common conditionally heteroskedastic factors among financial
assets through the GMM specification test. In such case, the Jacobian of the
moment conditions may be zero which leads to a slow rate of convergence of
the GMM estimator and a nonstandard asymptotic distribution of the Jtest
statistic. In this paper, we show that the zero Jacobian may provide
nontrivial local identification for unknown parameters. By exploiting such
information in estimation, we provide GMM estimator and specification tests
with standard properties. The standard properties are attractive because the
resulting GMM estimation and inference are more accurate in finite samples. 10.
"Nonparametric Instrumental Variables
and Regular Estimation", (with Jinyong Hahn), Econometric Theory, Vol.34(3), 2018,
pp. 574597. This
paper investigates whether there can exist regular estimators in models
characterized by nonparametric instrumental variable (NPIV). We show by a number of examples that regular estimation is impossible
in general for nonlinear functionals. 
11. "Shrinkage
Estimation of HighDimensional Factor Models with Structural Instabilities",
(with Xu Cheng and Frank Schorfheide), Review of Economic Studies, Vol.83(4), 2016, pp. 15111543. [Supplemental Appendix] In
this paper, we study panel data models with latent factors where the number
of factors and their loadings may change due to structural breaks in the
economy. We provide a LASSO estimation that consistently determines the
numbers of pre and postbreak factors, and the stability of factor loadings.
A novel feature of the LASSO estimator is its robustness to unknown break
date, whereas existing procedures either overestimate the number of factors
by neglecting the breaks or require known break dates. We apply the new LASSO
estimation to the recent (20072009) recession to investigate the stability
of factor loadings and the emergence of new factors. Using the U.S. data, our
procedure detects an increase in the number of factors at the onset of the
recession and a substantial change in the factor loadings. The series in the
categories of interest rate levels, interest rate spreads, and employment and
unemployment experience big changes in their factor loadings. The new factor
mainly effects the financial sector (e.g., the series in the categories of
interest rate levels, interest rate spreads, money and credit, stock prices
and wealth, and exchange rate), but also generate spillovers to the real
economy, which is consistent with the narratives of the recession. 
12. "Sieve
Semiparametric TwoStep GMM Under Weak Dependence",
(with Xiaohong Chen), Journal
of Econometrics, Vol.189(1),
2015, 163186. Many recently introduced empirical
methodologies adopt semiparametric twostep estimation approaches, where
certain functions are estimated nonparametrically in the first step, and some
Euclidean parameters are estimated parametrically in the second step using
the nonparametric estimates from the first stage as inputs. In this paper, we
consider semiparametric twostep GMM estimation and inference with weakly
dependent data, where unknown nuisance functions are estimated via sieve
extremum estimation in the first step. We show that although the asymptotic
variance of the secondstep GMM estimator may not have a closed form
expression, it can be well approximated by sieve variances that have simple
closed form expressions. We present both consistent and robust longrun
variance estimators, Wald tests and Hansen’s (1982) overidentification tests
for the secondstep GMM that properly reflect the firststep estimated
functions and the weak dependence of the data. Our sieve semiparametric
twostep GMM inference procedures are shown to be numerically equivalent to
the ones computed as if the first step were parametric. 
13. "Select the Valid
and Relevant Moments: An Informationbased LASSO for GMM with Many Moments",
(with Xu Cheng), Journal
of Econometrics, Vol.186
(2), 2015, 443464. [Supplemental
Appendix] This paper studies the selection of valid and
relevant moments for the GMM estimation. For applications with many candidate
moments, our asymptotic analysis accommodates a diverging number of moments
as the sample size increases. We refine the LASSO penalty in Liao (2013) such
that the new penalty signals both moment validity and relevance for
consistent moment selection. The proposed procedure achieves three objectives
in a onestep GMM LASSO estimation: (i) the valid
and relevant moments are distinguished from the invalid or irrelevant ones;
(ii) all desired moments are selected in one step instead of in a stepwise
manner; (iii) the parameters of interest are automatically estimated with all
selected moments as opposed to a postselection estimation. We develop
asymptotic results for the highdimensional GMM LASSO estimator, allowing for
nonsmooth sample moments and weakly dependent observations. 
14. "Automated
Estimation of Vector Error Correction Models",
(with Peter C.B. Phillips), Econometric Theory, Vol.31(3), 2015, 581646. [Supplemental Appendix] Model selection and
associated issues of postmodel selection inference present well known
challenges in empirical econometric research. These modeling issues are
manifest in all applied work, but they are particularly acute in multivariate
time series settings such as cointegrated systems where multiple
interconnected decisions can materially affect the form of the model and its
interpretation. In cointegrated system modeling, empirical estimation
typically proceeds in a stepwise manner that involves the determination of
cointegrating rank and autoregressive lag order in a reduced rank vector
autoregression followed by estimation and inference. This paper proposes an
automated approach to cointegrated system modeling that uses adaptive
shrinkage techniques to estimate vector error correction models with unknown
cointegrating rank structure and unknown transient lag dynamic order. These
methods enable simultaneous order estimation of the cointegrating rank and
autoregressive order in conjunction with oraclelike efficient estimation of
the cointegrating matrix and transient dynamics. 
15. "Asymptotic
Efficiency of Semiparametric Twostep GMM",
(with Daniel Ackerberg, Xiaohong Chen, Jinyong Hahn), Review of Economic Studies, Vol.81(3), 2014, 919943. In
many momentbased econometric models, there are both unknown functions and
finite dimensional parameters in the moment conditions. The onestep GMM
estimation that estimates them simultaneously is theoretically feasible, but
it may be difficult to implement in practice because there are too many
parameters to estimate. Therefore, the twostep GMM estimation is more
attractive. One estimates the unknown functions in the first step and
estimates the finite dimensional parameters only in the second step. In this
paper, we study the efficiency of the twostep GMM
estimator. We characterize the semiparametric efficiency bound for a large
class of semiparametric models and show that the twostep GMM estimator
achieves this efficiency bound, where the nuisance functions could be
estimated via any consistent nonparametric procedures in the firststep. This
result shows that the widely used semiparametric twostep GMM estimator in
empirical studies is not only computationally convenient, but also as
efficient as the onestep GMM estimator. 
16. "Sieve M Inference
of Irregular Parameters", (with
Xiaohong Chen), Journal
of Econometrics, Vol.182(1),
2014, 7086.
This paper presents sieve inferences on possibly irregular (i.e.,
slower than rootn estimable) functionals of seminonparametric models with i.i.d. data. We provide a simple consistent variance
estimator of the plugin sieve M estimator of a possibly irregular
functional, and the asymptotic standard normality of the sieve t statistic.
We show that, for hypothesis testing of irregular functionals, the sieve
likelihood ratio statistic is asymptotically Chisquare distributed. These
results complement Chen, Liao and Sun (2014) and are useful in inference on
structural parameters that may have singular semiparametric efficiency
bounds. 
17. "Sieve Inference on
Possibly Misspecified Seminonparametric Time Series Models",
(with Xiaohong Chen and Yixiao Sun), Journal of Econometrics, Vol.178(3), 2014, 639658. This paper establishes the
asymptotic normality of plugin sieve M estimators of possibly irregular
functionals of seminonparametric time series models. We show that, even when
the sieve score process is not a martingale difference sequence, the asymptotic
variance in the case of irregular functionals is the same as those for
independent data. Using an orthonormal series long run variance estimator, we
construct a “preasymptotic” Wald statistic and show that it is
asymptotically F distributed. Simulations indicate that our “preasymptotic”
Wald test with F critical values has more accurate size in finite samples
than the conventional Wald test with chisquare critical values. 
18. "Adaptive GMM
Shrinkage Estimation with Consistent Moment Selection",
Econometric Theory, Vol.29, 2013, 148. [Early Version] The generalized method of
moments (GMM) is a popular method to estimate economic models since
restrictions implied by economic theories usually take the form of moment
conditions. The validity of the moment conditions is very important because
the GMM estimator based on invalid (misspecified) moment conditions may be
inconsistent, i.e., the estimator is far from its true value even for a large
dataset. In this paper, we provide a simple method which employs the LASSO
method to select valid moment conditions. The key step is to construct a
criterion that features a novel datadependent penalty for each moment
condition. The method selects a moment condition only if it can improve the
model fit by more than this penalty. Compared to the conventional method that
selects moment conditions one by one, this new method selects them
simultaneously. Thus, it not only yields a much faster algorithm but also is
particularly useful when there are many moment conditions to select. 
19. "Series
Estimation of Stochastic Processes: Recent Developments and Econometric
Applications",
(with Peter C.B. Phillips)
in A. Ullah, J. Racine and L. Su (eds.) Handbook of Applied Nonparametric
and Semiparametric Econometrics and
Statistics, Oxford University Press, 2013 
20. "Asymptotic
Properties of Penalized M Estimators with Time Series Observations",
(with Xiaohong Chen), in N.R. Swanson and X. Chen
(eds.) Recent Advances and Future
Directions in Causality, Prediction, and Specification Analysis: Essays in
Honor of Halbert L. White Jr, Springer, 2013 