Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2020
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.
Keywords
Classifier Lasso, Dynamic OLS, Heterogeneity, Latent group structure, Nonstationarity, Penalized least squares, Panel cointegration, Purchasing power
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Theory
Volume
36
Issue
3
First Page
410
Last Page
456
ISSN
0266-4666
Identifier
10.1017/S0266466619000197
Publisher
Cambridge University Press
Embargo Period
11-15-2021
Citation
HUANG, Wenxin; JIN, Sainan; and SU, Liangjun.
Identifying latent grouped patterns in conintegrated panels. (2020). Econometric Theory. 36, (3), 410-456.
Available at: https://ink.library.smu.edu.sg/soe_research/2500
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/S0266466619000197