Yiren Wang, Peter C B Phillips, Liangjun Su
arXiv 29 Jul 2023 · Econometrics · publishedJournal of Econometrics (2024) · 15 citations (OpenAlex)
arXiv:2307.15863 · PDF · DOI · OpenAlex · Extracted main text
This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism the model may have different numbers of groups and/or different group memberships before and after the break. With the preliminary nuclear-norm-regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is shown that the break point, the number of groups and the group memberships can each be estimated correctly with probability approaching one. Asymptotic distributions of the estimators of the slope coefficients are established. Monte Carlo simulations demonstrate excellent finite sample performance for the proposed estimation algorithm. An empirical application to real house price data across 377 Metropolitan Statistical Areas in the US from 1975 to 2014 suggests the presence both of structural breaks and of changes in group membership.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Su, L., Shi, Z., and Phillips, P. C. B (2016) Identifying latent structures in panel data self | 0.843 | 3 | 3 | 100% |
| 2 | Chernozhukov, V., Hansen, C. B., Liao, Y., and Zhu, Y (2020) Inference for heterogeneous effects using low-rank estimations | 0.769 | 11 | 7 | 45% |
| 3 | Su, L. and Chen, Q (2013) Testing homogeneity in panel data models with interactive fixed effects self | 0.737 | 5 | 4 | 40% |
| 4 | Jin, J., Ke, Z. T., Luo, S., and Wang, M (2022) Optimal estimation of the number of network communities | 0.737 | 4 | 3 | 50% |
| 5 | Lumsdaine, R. L., Okui, R., and Wang, W (2023) Estimation of panel group structure models with structural breaks in group memberships and coefficients | 0.693 | 6 | 1 | 100% |
| 6 | Okui, R. and Wang, W (2021) Heterogeneous structural breaks in panel data models | 0.693 | 6 | 1 | 100% |
| 7 | Moon, H. R. and Weidner, M (2017) Dynamic linear panel regression models with interactive fixed effects | 0.679 | 16 | 4 | 31% |
| 8 | Bai, J (2009) Panel data models with interactive fixed effects | 0.659 | 7 | 5 | 29% |
| 9 | Hong, S., Su, L., and Jiang, T (2023) Profile gmm estimation of panel data models with interactive fixed effects self | 0.644 | 2 | 2 | 100% |
| 10 | Lu, X. and Su, L (2016) Shrinkage estimation of dynamic panel data models with interactive fixed effects self | 0.585 | 5 | 3 | 20% |
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