Jian Hong

Ph.D. Candidate

Economics, Penn State University

I am a Ph.D. candidate at Department of Economics, Penn State University. My research focuses on Theoretical and Applied Econometrics, and Industrial Organization. My advisor is Professor Quang Vuong and my job market paper is:

"Nonparametric Identification and Estimation of Production Functions Using Control Function Approaches to Endogeneity." (PDF)

Contact:

Department of Economics
608 Kern Graduate Building
Pennsylvania State University
University Park, PA 16802

Telephone: 814-360-2934 (Cell)
E-mail:       jhong@psu.edu
Website:   www.personal.psu.edu/jzh109/

Vistors #: 554

Curriculum Vitae (Updated, PDF)

CITIZENSHIP:
 China (F-1 visa)

EDUCATION:
 Ph.D., Economics, Penn State University, expected Summer 2008
 M.A., Economics, Peking University, China, 2001
 B.S., Information Technology, Beijing Normal University, China, 1997

PH.D. THESIS:
 Nonparametric Estimation and Testing Using Control Function Approaches
 Thesis Advisor: Professor Quang Vuong

FIELDS:
 Primary: Applied and Theoretical Econometrics
 Secondary: Industrial Organization, Applied Microeconomics


PAPERS:
 “Nonparametric Identification and Estimation of Production Functions Using Control Function Approaches to Endogeneity,” 2007 (Job Market Paper)

 “Semiparametric Identification and Estimation of Production Functions,” 2007

 “A Nonparametric Hausman Test of Exogeneity,” 2007 (in progress)

 “Residential Electricity Consumption under Real-time Pricing,” 2007

 “Identification in Empirical Auctions,” 2004

TEACHING EXPERIENCE:
 Instructor:
Intermediate Macroeconomics
 Teaching Assistant:
Intermediate Microeconomics, Money and Banking, Principles of Economics, Principles of Macroeconomics

RESEARCH EXPERIENCE:
 Research Assistant, Summer 2002, 2004 for Professor Susanna Esteban
 Research Assistant, Summer 2003 for Professor Gustavo Ventura

PRESENTATIONS: Applied Microeconomics Workshop, Penn State, Summer 2005

AWARDS: Graduate Scholar Award, Penn State, 2002-2003

REFERENCES:
 Professor Quang Vuong, Penn State, (qxv1@psu.edu)
 Professor Mark Roberts, Penn State, (mroberts@psu.edu)
 Professor Isabelle Perrigne, Penn State, (iup2@psu.edu)
 Professor Joris Pinkse, Penn State, (joris@psu.edu)

Papers

Nonparametric Identification and Estimation of Production Functions Using Control Function Approaches to Endogeneity(Job Market Paper)

Using control function approaches to endogeneity, nonparametric identification is established for production functions under weak conditions. The distribution of productivity is also recovered nonparametrically. Instead of "inverting out" the productivity shock, control functions "expect out" unobserved shocks. Controls are estimated using lagged levels of capital and labor as instruments, and the control function condition is justified by a Markov property of productivity shock along with uncertainty faced by firms. Nonparametric estimation of production functions then closely follows the identification strategy without imposing extra modeling assumptions. A kernel estimator is proposed for nonparametric regressions with endogeneity. The estimator achieves the optimal uniform convergence rate if the preliminary estimators of controls converge sufficiently fast. The finite sample performance is illustrated by extensive Monte-Carlo experiments. The application to the Chilean panel shows the empirical relevance. The proposed method yields reasonable estimates, and the empirical distribution of productivity is non-normal. This paper extends the methods of Olley and Pakes (1996) and Levinsohn and Petrin (2003), and also provides a kernel-based alternative to the series estimators proposed by Newey, Powell, and Vella (1999).

Semiparametric Identification and Estimation of Production Functions (Coming soon)

The identification strategy using control function approaches to endogeneity proposed in Essay 1 is adapted to the Cobb-Douglas production function. A partial linear model arises naturally where the parametric part represents the production function and the nonparametric part is the control function to "expect out" unobserved shocks. With appropriate choices of controls, both capital and labor coefficients can be identified and estimated simultaneously under reasonable timing assumptions. In this case, the estimator achieves Root-N-Consistency and the computation is simple in that there is no iterative optimization algorithm involved. When only cross-sectional data is available, parametric identification is still possible. The coefficients can be estimated by moment conditions based on the non-transmitted error. The empirical distribution of productivity can also be recovered. The performance of semiparametric estimation procedures in finite samples is demonstrated by Monte-Carlo experiments. The procedures are applied again to the Chilean panel data and result in reasonable estimates of capital and labor coefficients.

A Nonparametric Hausman Test of Exogeneity (in progress)

When exogeneity is assumed in nonparametric regression models, it is natural to test whether this assumption is valid. Many parametric tests are inconsistent as they only have power against certain alternatives. A nonparametric Hausman test of exogeneity is proposed, which is consistent and has power in every direction. Following the idea of the original Hausman test, this test compares the nonparametric estimator of the conditional mean to a consistent estimator that takes the potential endogeneity into account. For the latter, a kernel estimator derived by control function approaches is used. These two estimators converge to each other when the regressors are exogenous, and diverge otherwise. Thus, the weighted integrated square difference between the two estimators can serve as a test statistic. The asymptotical normality of the normalized test statistic is established using U-statistics. The performance of the test statistic is examined by a Monte-Carlo study, in which the test is able to detect sequences of local alternatives.

Residential Electricity Consumption under Real-time Pricing

This paper studies the response of residential households’ electricity consumption to the price of electricity in real-time pricing scheme. The micro panel dataset comes from the Allegheny Power Demand Side Response Program. Robinson’s (1988) partially linear model is extended to the dynamic panel. A nonparametric function captures the effects of electricity prices and the temperature on electricity consumption, which are found to be nonlinear and interact with each other. A linear parametric specification is used on an additional group of control variables. The participating residential households react to real-time prices when the temperature is not too high.

Identification in Empirical Auctions

This paper provides formal proofs of the propositions in the paper “Structural Analysis of Auction Data” by Laffont and Vuong (1996, AER). Identification of structural elements of auction models are examined for both symmetric and asymmetric cases, and for both private value and common value paradigms. Testable restrictions imposed by auction models and distinguishability among models are also studied.

This is originally a term paper for "Empirical Auction" taught by Professors Perrigne. This is also a salute to my advisor Dr. Vuong, and a memory of Dr. Laffont, who is one of the best economists of his generation but left us too early.

Teaching

Instructor:

Intermediate Macroeconomics (Econ 304)

Teaching Assistant:

Intermediate Microeconomics, Money and Banking, Principles of Economics, Principles of Macroeconomics