Wei Wang, Xiaodong Yan, Yanyan Ren, Zhijie Xiao
arXiv 20 Oct 2021 · Econometrics · publishedEconomics Letters (2023) · 1 citations (OpenAlex)
arXiv:2110.10480 · PDF · DOI · OpenAlex · Extracted main text
Heterogeneous panel data models that allow the coefficients to vary across individuals and/or change over time have received increasingly more attention in statistics and econometrics. This paper proposes a two-dimensional heterogeneous panel regression model that incorporate a group structure of individual heterogeneous effects with cohort formation for their time-variations, which allows common coefficients between nonadjacent time points. A bi-integrative procedure that detects the information regarding group and cohort patterns simultaneously via a doubly penalized least square with concave fused penalties is introduced. We use an alternating direction method of multipliers (ADMM) algorithm that automatically bi-integrates the two-dimensional heterogeneous panel data model pertaining to a common one. Consistency and asymptotic normality for the proposed estimators are developed. We show that the resulting estimators exhibit oracle properties, i.e., the proposed estimator is asymptotically equivalent to the oracle estimator obtained using the known group and cohort structures. Furthermore, the simulation studies provide supportive evidence that the proposed method has good finite sample performance. A real data empirical application has been provided to highlight the proposed method.
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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 | Ma and Huang (2016) Estimating subgroup-specific treatment effects via concave fusion | 0.928 | 4 | 3 | 100% |
| 2 | Ma and Huang (2017) A Concave Pairwise Fusion Approach to Subgroup Analysis | 0.843 | 3 | 3 | 100% |
| 3 | Okui and Wang (2020) Heterogeneous structural breaks in panel data models | 0.843 | 3 | 3 | 100% |
| 4 | Fan and Li (2001) Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties | 0.644 | 2 | 2 | 100% |
| 5 | Qian and Su (2016) Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso | 0.644 | 2 | 2 | 100% |
| 6 | Zhang (2010) NEARLY UNBIASED VARIABLE SELECTION UNDER MINIMAX CONCAVE PENALTY | 0.644 | 2 | 2 | 100% |
| 7 | Browning and Carro (2010) Heterogeneity in dynamic discrete choice models | 0.405 | 1 | 1 | 100% |
| 8 | Belzil and Hansen (2002) Unobserved ability and the return to schooling | 0.405 | 1 | 1 | 100% |
| 9 | Bonhomme and Manresa (2015) Grouped Patterns of Heterogeneity in Panel Data | 0.405 | 1 | 1 | 100% |
| 10 | Bai (2010) Common breaks in means and variances for panel data | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.