Ruiqi Liu, Ben Boukai, Zuofeng Shang
arXiv 20 Nov 2019 · Econometrics
arXiv:1911.08830 · PDF · DOI · OpenAlex · Extracted main text
A new statistical procedure, based on a modified spline basis, is proposed to identify the linear components in the panel data model with fixed effects. Under some mild assumptions, the proposed procedure is shown to consistently estimate the underlying regression function, correctly select the linear components, and effectively conduct the statistical inference. When compared to existing methods for detection of linearity in the panel model, our approach is demonstrated to be theoretically justified as well as practically convenient. We provide a computational algorithm that implements the proposed procedure along with a path-based solution method for linearity detection, which avoids the burden of selecting the tuning parameter for the penalty term. Monte Carlo simulations are conducted to examine the finite sample performance of our proposed procedure with detailed findings that confirm our theoretical results in the paper. Applications to Aggregate Production and Environmental Kuznets Curve data also illustrate the necessity for detecting linearity in the partially linear panel model.
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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 | Xue, L (2009) Consistent variable selection in additive models | 0.843 | 3 | 3 | 100% |
| 2 | Fan, J. and Li, R (2001) Variable selection via nonconcave penalized likelihood and its oracle properties | 0.811 | 4 | 2 | 100% |
| 3 | Chen, X (2007) Large sample sieve estimation of semi-nonparametric models | 0.737 | 3 | 3 | 67% |
| 4 | Huang, J (1998) Projection estimation in multiple regression with application to functional anova models | 0.644 | 4 | 1 | 100% |
| 5 | Henderson, D. J., Carroll, R. J., and Li, Q (2008) Nonparametric estimation and testing of fixed effects panel data models | 0.644 | 4 | 1 | 100% |
| 6 | Li, D., Qian, J., and Su, L (2016) Panel data models with interactive fixed effects and multiple structural breaks | 0.585 | 3 | 1 | 100% |
| 7 | Su, L. and Chen, Q (2013) Testing homogeneity in panel data models with interactive fixed effects | 0.585 | 3 | 1 | 100% |
| 8 | Su, L. and Jin, S (2012) Sieve estimation of panel data models with cross section dependence | 0.585 | 3 | 1 | 100% |
| 9 | Su, L. and Zhang, Y (2015) Nonparametric dynamic panel data models with interactive fixed effects: sieve estimation and specification testing | 0.585 | 3 | 1 | 100% |
| 10 | Su, L. and Zhang, Y (2016) Semiparametric estimation of partially linear dynamic panel data models with fixed effects | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 59 scored citations.