arXiv 25 Apr 2023 · Econometrics · publishedEconometrics Journal (2024) · 2 citations (OpenAlex)
arXiv:2304.13199 · PDF · DOI · OpenAlex · Extracted main text
This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects (CCE) framework. The proposed two-step estimation method involves applying principal component analysis to estimate latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and factor loadings in the second step. The asymptotic distributions of the proposed estimators are derived under general conditions, assuming that the number of time-series observations is comparable to the number of cross-sectional observations. To correct for asymptotic biases of the estimators, we introduce both analytical and split-panel jackknife methods, and confirm their good performance in finite samples using Monte Carlo simulations. An empirical application utilizes the proposed method to study the arbitrage behaviour of nonfinancial firms across different security markets.
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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 | Chen, M., I. Fernández-Val, and M. Weidner (2021) Nonlinear factor models for network and panel data | 1.000 | 14 | 3 | 100% |
| 2 | Hahn, J. and W. Newey (2004) Jackknife and analytical bias reduction for nonlinear panel models | 1.000 | 6 | 4 | 100% |
| 3 | Dhaene, G. and K. Jochmans (2015) Split-panel jackknife estimation of fixed-effect models | 0.928 | 4 | 3 | 100% |
| 4 | Chen, L (2022) Two-step estimation of quantile panel data models with interactive fixed effects self | 0.874 | 6 | 2 | 100% |
| 5 | Ando, T., J. Bai, and K. Li (2022) Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity | 0.811 | 4 | 2 | 100% |
| 6 | Bai, J (2009) Panel data models with interactive fixed effects | 0.811 | 4 | 2 | 100% |
| 7 | Boneva, L. and O. Linton (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% |
| 8 | Gao, J., F. Liu, B. Peng, and Y. Yan (2023) Binary response models for heterogeneous panel data with interactive fixed effects | 0.811 | 4 | 2 | 100% |
| 9 | Karabiyik, H., S. Reese, and J. Westerlund (2017) On the role of the rank condition in CCE estimation of factor-augmented panel regressions | 0.737 | 3 | 2 | 100% |
| 10 | Chen, M (2016) Estimation of nonlinear panel models with multiple unobserved effects | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 39 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 0.5cmLow-Rank Estimation of Nonlinear Panel Data Models | 0.405 | 1 | 1 |
| 2 | Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects | 0.405 | 1 | 1 |