Mingli Chen, Iván Fernández-Val, Martin Weidner
arXiv 17 Dec 2014 · Statistics — Methodology · publishedJournal of Econometrics (2020) · 82 citations (OpenAlex)
arXiv:1412.5647 · PDF · DOI · OpenAlex · Extracted main text
Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications. We establish that fixed effect estimators of model parameters and average partial effects have normal distributions when the two dimensions of the panel grow large, but might suffer of incidental parameter bias. We show how models with factor structures can also be applied to capture important features of network data such as reciprocity, degree heterogeneity, homophily in latent variables and clustering. We illustrate this applicability with an empirical example to the estimation of a gravity equation of international trade between countries using a Poisson model with multiple factors.
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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 | Moon, H. R. and Weidner, M (2015) Linear regression for panel with unknown number of factors as interactive fixed effects self | 1.000 | 5 | 4 | 100% |
| 2 | Ando, T. and Bai, J (2016) Large scale panel choice model with unobserved heterogeneity | 1.000 | 5 | 3 | 100% |
| 3 | Chen, M (2014) Estimation of nonlinear panel models with multiple unobserved effects self | 0.928 | 4 | 3 | 100% |
| 4 | Ahn, S. C. and Horenstein, A. R (2013) Eigenvalue ratio test for the number of factors | 0.874 | 6 | 2 | 100% |
| 5 | Fernández-Val, I. and Weidner, M (2018) Fixed effects estimation of large-T panel data models self | 0.843 | 4 | 4 | 75% |
| 6 | Bai, J (2009) Panel data models with interactive fixed effects | 0.843 | 4 | 3 | 75% |
| 7 | Fernández-Val, I. and Weidner, M (2016) Individual and time effects in nonlinear panel models with large n, t self | 0.825 | 16 | 3 | 56% |
| 8 | Boneva, L. and Linton, O (2017) A discrete-choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of… | 0.811 | 4 | 2 | 100% |
| 9 | Helpman, E., Melitz, M., and Rubinstein, Y (2008) Estimating trade flows: trading partners and trading volumes | 0.737 | 3 | 2 | 100% |
| 10 | Anderson, J. E. and van Wincoop, E (2003) Gravity with gravitas: A solution to the border puzzle | 0.644 | 2 | 2 | 100% |
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