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Identifying Latent Grouped Patterns in Cointegrated Panels

Wenxin Huang (), Sainan Jin and Liangjun Su
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Wenxin Huang: Antai College of Economics and Management, Shanghai Jiao Tong University

No 3-2019, Economics and Statistics Working Papers from Singapore Management University, School of Economics

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) 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 ones 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.

Keywords: Classifier Lasso; Dynamic OLS; Heterogeneity; Latent group structure; Nonstationarity; Penalized least squares; Panel cointegration; Purchasing power parity (search for similar items in EconPapers)
JEL-codes: C13 C33 C51 F31 (search for similar items in EconPapers)
Pages: 76 pages
Date: 2018-11-20
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-sea
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Journal Article: IDENTIFYING LATENT GROUPED PATTERNS IN COINTEGRATED PANELS (2020) Downloads
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