Abstract
This paper develops a new model and estimation procedure for panel data that allows us to identify heterogeneous structural breaks. We model individual heterogeneity using a grouped pattern. For each group, we allow common structural breaks in the coefficients. However, the number, timing, and size of these breaks can differ across groups. We develop a hybrid estimation procedure of the grouped fixed effects approach and adaptive group fused Lasso. We show that our method can consistently identify the latent group structure, detect structural breaks, and estimate the regression parameters. Monte Carlo results demonstrate the good performance of the proposed method in finite samples. An empirical application to the relationship between income and democracy illustrates the importance of considering heterogeneous structural breaks.
| Original language | English |
|---|---|
| Pages (from-to) | 447-473 |
| Number of pages | 27 |
| Journal | Journal of Econometrics |
| Volume | 220 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 18 Jan 2021 |
Bibliographical note
Funding Information:The authors would also like to thank the guest editor (Tom Wansbeek), two anonymous referees, Otilia Boldea, Pavel Cizek, Qu Feng, Cheng Hsiao, Arthur Lewbel, Robin Lumsdaine, Elena Manresa, Liangjun Su, Martin Weidner and participants at various seminars and conferences for their valuable comments and suggestions. This project commenced when Okui was at Vrije Universiteit Amsterdam and Kyoto University and a part of it was conducted while Okui was at NYU Shanghai. This work was supported by the Japan Society for the Promotion of Science (JSPS) under KAKENHI Grant [Nos. 16K03598 and 15H03329]; New Faculty Startup Grant of Seoul National University; the Housing and Commercial Bank Economic Research Fund for Institute of Economic Research of Seoul National University; and an Erasmus University Rotterdam fellowship.
Funding Information:
This work was supported by the Japan Society for the Promotion of Science (JSPS) under KAKENHI Grant [Nos. 16K03598 and 15H03329 ]; New Faculty Startup Grant of Seoul National University ; the Housing and Commercial Bank Economic Research Fund for Institute of Economic Research of Seoul National University ; and an Erasmus University Rotterdam fellowship .
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© 2020 Elsevier B.V.