Peng Shao

Peng Shao
Job Market Paper

Semiparametric Panel Model with Group Heterogeneity

This paper studies a semiparametric partially linear panel model with time-varying group-level effects. As a critical feature, the group memberships are unobserved but time-invariant. The linear coefficients estimator is shown asymptotically normal for inference. For production function estimation, the paper also considers a two-step problem; the objective (second-step) parameter is identified by moments, conditional on the partially linear model’s potentially infinite-dimensional parameters. The paper proposes a second-step estimator and shows that it is consistent. The two analyses generically connect to the control function problem under the presence of time-varying heterogeneity for panel models. With the two-step solution, the paper extends the proxy variable method, designed for the simultaneity problem with estimating the firm’s production function, by allowing cross-correlation in firms’ productivity. As an empirical application, I consider four Chilean manufacturing sectors from 1987 to 1996. After accounting for cross-correlated productivity, I find larger productivity effects on output growth and more heterogeneous productivity among firms.

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Other Research

Clustering for Multidimensional Heterogeneity with Application to the Production Function (with Xu Cheng and Frank Schorfheide)

This paper provides a new multi-clustering approach for multi-dimensional unobserved heterogeneity in panel data methods. Each unit is assigned with cluster memberships in differing features. For example, two firms can share their capital output elasticity but differ in labour output elasticity and mean productivity. The memberships are unknown and classified simultaneously. In contrast, the existing approach ignores firms can share some but not all features. Our multi-clustering approach provides significant improvements when these multiple unobserved features have a sparse interaction, i.e., only a small number of firms share all features. We estimate the multi-clustering memberships and the unknown cluster-specific and common parameters in a nonlinear GMM problem. Furthermore, we provide the first classification consistency result in a nonlinear GMM setup. We re-evaluate the rise of mark-up in De Loecker and Eeckhout (2017) by replacing their sector-specific production function with a cluster-based one. We find that the upward trajectory persists, but the magnitude is less pronounced after accounting for multi-dimensional heterogeneity.

Matching to Produce Information (with Ashwin Kambhampati and Carlos Segura-Rodriguez)

We posit a model of endogenous team formation to study the incentives of heterogeneous workers inside organizations that decentralize information production and characterize the inefficiencies that arise in equilibrium. Our analysis leads us to define, and prove the existence of, a new solution concept for one-sided matching environments with imperfectly transferable utility, Coalitional Subgame Perfect Equilibrium. Inefficient equilibrium sorting arises from two sources. First, moral hazard within teams may cause workers to join less productive teams in which they exert less effort. Second, while some workers may have incentives to form productive teams, such teams may generate significant negative externalities on the productivity of other teams.

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Teaching Experience


Econometrics I, Teaching Assistant for Prof. Frank Schorfheide and Xu Cheng (Fall 2015-2016)


Econometrics, University of Pennsylvania, Teaching Assistant for Professor Frank Diebold (Fall 2018, 2017, Spring 2018, 2019)

Econometrics, University of Pennsylvania, Teaching Assistant for Professor Xu Cheng (Spring 2017)

Introduction to Macroeconomics, University of Pennsylvania, Teaching Assistant for Dr. Luca Bossi (Spring 2016)


Econometrics, Industrial Organization


+1-(267) 418-8373


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Frank Schorfheide

Xu Cheng


Professor Frank Schorfheide (Advisor)

Department of Economics

University of Pennsylvania

Phone: +1 (215) 898-8486



Professor Xu Cheng (Advisor)

Department of Economics

University of Pennsylvania

Phone: +1 (215) 898-6775



Professor Amit Gandhi

Department of Economics and The Wharton School

University of Pennsylvania

Phone: +1 (215) 898-7409


Job Market Candidate Status
I am on the job market and I will be available for interviews at the 2020 ASSA Annual Meeting in San Diego.