Jushan Bai, Pablo Mones
arXiv 19 Apr 2025 · Econometrics
arXiv:2504.14354 · PDF · Extracted main text
This paper examines the problem of global identification in dynamic panel models with interactive effects, a fundamental issue in econometric theory. We focus on the setting where the number of cross-sectional units (N) is large, but the time dimension (T) remains fixed. While local identification based on the Jacobian matrix is well understood and relatively straightforward to establish, achieving global identification remains a significant challenge. Under a set of mild and easily satisfied conditions, we demonstrate that the parameters of the model are globally identified, ensuring that no two distinct parameter values generate the same probability distribution of the observed data. Our findings contribute to the broader literature on identification in panel data models and have important implications for empirical research that relies on interactive effects.
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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 | Hayakawa, K., Pesaran, M. H., and Smith, L. V (2023) Short t dynamic panel data models with individual, time and interactive effects | 1.000 | 10 | 3 | 100% |
| 2 | Anderson, T. and Rubin, H (1956) Statistical inference in factor analysis | 1.000 | 9 | 6 | 100% |
| 3 | Arellano, M. and Bond, S (1991) Some tests of specification for panel data: Monte carlo evidence and an application to employment equations | 0.737 | 3 | 2 | 100% |
| 4 | Blundell, R. and Bond, S (1998) Initial conditions and moment restrictions in dynamic panel data models | 0.737 | 3 | 2 | 100% |
| 5 | Sentana, E (2024) Finite underidentification | 0.644 | 2 | 2 | 100% |
| 6 | Bai, J (2024) Likelihood approach to dynamic panel models with interactive effects self | 0.585 | 3 | 1 | 100% |
| 7 | Ahn, S. C., Lee, Y. H., and Schmidt, P (2013) Panel data models with multiple time-varying individual effects | 0.511 | 2 | 1 | 100% |
| 8 | Pesaran, M. H (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure | 0.511 | 2 | 1 | 100% |
| 9 | Williams, B (2020) Identification of the linear factor model | 0.511 | 2 | 1 | 100% |
| 10 | Ahn, S. C., Lee, Y. H., and Schmidt, P (2001) Gmm estimation of linear panel data models with time-varying individual effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.