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3620 S. Vermont Ave,
Kaprielian Hall 300, University of Southern California
Los Angeles, CA 90089-0001
Phone: 1-310-666-7685
Email: chengzho@usc.edu
Website: http://www-scf.usc.edu/˜chengzho/
University of Southern California
Ph. D. candidate, Economics,
Peking University
M. A., Economics
Hunan Normal University
B. A., Economics
Los Angeles, US expected May 2016
Beijing, China
July 2010
Changsha, China
July 2006
My research interests are econometrics, in particular microeconometrics, and its applications in labor economics and empirical industrial organization.
Title: Identification and Linear Estimation of General Dynamic Programming Discrete Choice Models
Advisor: Geert Ridder
This paper studies the nonparametric identification and estimation of the structural parameters, including the per period utility functions, discount factors, and state transition laws, of general dynamic programming discrete choice
(DPDC) models. I show an equivalence between the identification of general DPDC model and the identification of linear GMM system. Using such an equivalence, I simplify both the identification analysis and the estimation practice of DPDC model. First, I prove a series of identification results for the DPDC model by using rank conditions. Previous identification results in the literature are based on normalizing the per period utility functions of one alternative. Such normalization could severely bias the estimates of counterfactual policy effects. I show that the structural parameters can be nonparametrically identified without the normalization. Second, I propose a closed form nonparametric estimator for the per period utility functions, of which computation involves only least square estimation. The existing estimation procedures rely on assuming that the dynamic programming (DP) problem is stationary or on solving the
DP problem numerically with the aid of terminal conditions. Neither the identification nor the estimation requires terminal conditions, the DPDC model to be stationary, or having a sample that covers the entire decision horizon.
Geert Ridder (Chair)
Department of economics, USC ridder@usc.edu
Cheng Hsiao
Department of economics, USC chowderlad@gmail.com
Han Hong
Department of economics, Stanford doubleh@stanford.edu
Yu-Wei Hsieh
Department of economics, USC yuwei.hsieh@usc.edu

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1.
Estimation of Identified Sets in Nonlinear Panel Data Models , Aug, 2013.
For nonlinear panel data models, identification is a nontrivial issue because the distribution of individual effects is unknown. It is usually difficult to check the identifiableness of nonlinear panel data models. Moreover, recent work has found many widely used nonlinear panel models are only set identified. This paper propose a systematic approach to estimate the identified set of nonlinear panel data models. When the model is point identified, the identified set consists solely of the true parameters value. The proposed set estimator is consistent with n → ∞ and T being fixed. The convergence rate of the distance between the identified set and its estimate is O p
( n
δ/ 2
),
0 < δ < 1 /K , with K being the dimension of the unknown parameters. Simulation examples corroborate the theoretical findings.
2.
Econometrics of buy-price English auctions , Oct, 2010.
This paper provides a framework for the empirical study of buy-price English auctions. Within the conditionally independent private value paradigm, I show how to identify the underlying distribution of bidders valuations from the observed bids and the observed ratio of the number of auctions ended at buy price to the number of auctions in the whole sample. The paper then develops a smooth monotone polynomial spline (PS) estimator for the latent distribution of private valuations. Under mild conditions, both L
2 and uniform convergence rates of the estimator are obtained. The pointwise asymptotic normality of the proposed estimator is also established.
Basing on the contraction mapping theorem, I propose an fast iterative algorithm to implement the proposed PS estimator.
1. Identification and Closed-Form Estimation of Dynamic Programming Discrete Choice Models with Hidden Types
2. Nonparametric Identification and Estimation of Dynamic Programming Discrete Choices Models with Endogeneity
1. “Market Timing: Recent Development and a New Test”, Economics Letters , 2011, 111, 105–109. (with Chia-
Shang J. Chu)
2. “Testing Independence of Two Autocorrelated Binary Time Series”, Statistics and Probability Letters , 2010, 80,
69-75. (with Chia-Shang J. Chu)
1. “Identification and Linear Estimation of General Dynamic Programming Discrete Choice Models”, The 2015
California Econometrics Conference , Los Angeles, US, September, 2015.
2. “Counterfactual Policy Predictions using Dynamic Discrete Choice Models without Normalization of Per Period
Utilities”, The 2015 World Congress of Econometric Society , Montreal, Canada, August, 2015.
• Teaching Assistant.
ECON615: Applied Econometrics, Professor Cheng Hsiao, Fall 2013 and 2015, USC.
• Teaching Assistant.
ECON511: Econometric Methods, Professor Roger Moon, Spring 2013 and 2014, USC.
• Teaching Assistant.
ECON251: Microeconomics for Business, Lecturer Mohammad Safarzadeh, Spring 2012,
USC.
• Teaching Assistant.
ECON501: Macroeconomic Analysis and Policy, Associate Professor Mark Moore, Spring
2012, USC.
• The Best Third-Year Paper Award, Department of Economics, USC 2013
• The Best Second-Year Paper Award, Department of Economics, USC 2012
• College Graduate Merit Award, USC 2010