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IDENTIFYING LATENT GROUPED PATTERNS IN COINTEGRATED PANELS

Published online by Cambridge University Press:  22 July 2019

Wenxin Huang
Affiliation:
Shanghai Jiao Tong University
Sainan Jin
Affiliation:
Singapore Management University
Liangjun Su*
Affiliation:
Singapore Management University
*
*Address correspondence to Liangjun Su, School of Economics, Singapore Management University, 90 Stamford Road, Singapore 178903, Singapore; e-mail: ljsu@smu.edu.sg, Phone: +65 6828 0386.

Abstract

We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips (2016, Econometrica 84(6), 2215–2264) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We show that we can identify the individuals’ group membership and estimate the group-specific long-run cointegrated relationships simultaneously. We demonstrate the desirable property of uniform classification consistency and the oracle properties of both the C-Lasso estimators and their post-Lasso versions. The special case of dynamic penalized least squares is also studied. Simulations show superb finite sample performance in both classification and estimation. In an empirical application, we study the potential heterogeneous behavior in testing the validity of long-run purchasing power parity (PPP) hypothesis in the post–Bretton Woods period from 1975–2014 covering 99 countries. We identify two groups in the period 1975–1998 and three groups in the period 1999–2014. The results confirm that at least some countries favor the long-run PPP hypothesis in the post–Bretton Woods period.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2019 

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Footnotes

The authors sincerely thank the co-editor Anna Mikusheva and two anonymous referees for their many constructive comments on the early versions of the article. They also thank Peter C.B. Phillips and Qiying Wang for discussions on the subject matter and the participants in the 2017 Asian Meeting of the Econometric Society at CUHK and the 2017 Advances in Econometrics Conference at SJTU for their valuable comments. Su gratefully acknowledges the Singapore Ministry of Education for Academic Research Fund under Grant MOE2012-T2-2-021 and the funding support provided by the Lee Kong Chian Fund for Excellence. Huang gratefully acknowledges the funding support provided by the Shanghai Institute of International Finance and Economics.

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