arXiv 5 Nov 2019 · Econometrics · 1 citations (OpenAlex)
arXiv:1911.01824 · PDF · DOI · OpenAlex · Extracted main text
This paper considers panel data models where the conditional quantiles of the dependent variables are additively separable as unknown functions of the regressors and the individual effects. We propose two estimators of the quantile partial effects while controlling for the individual heterogeneity. The first estimator is based on local linear quantile regressions, and the second is based on local linear smoothed quantile regressions, both of which are easy to compute in practice. Within the large T framework, we provide sufficient conditions under which the two estimators are shown to be asymptotically normally distributed. In particular, for the first estimator, it is shown that $N<<T^{2/(d+4)}$ is needed to ignore the incidental parameter biases, where $d$ is the dimension of the regressors. For the second estimator, we are able to derive the analytical expression of the asymptotic biases under the assumption that $N\approx Th^{d}$, where $h$ is the bandwidth parameter in local linear approximations. Our theoretical results provide the basis of using split-panel jackknife for bias corrections. A Monte Carlo simulation shows that the proposed estimators and the bias-correction method perform well in finite samples.
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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 | Galvao, A. F. and K. Kato (2016) Smoothed quantile regression for panel data | 0.941 | 6 | 4 | 83% |
| 2 | Kato, K., A. F. Galvao, and G. V. Montes-Rojas (2012) Asymptotics for panel quantile regression models with individual effects | 0.874 | 9 | 3 | 67% |
| 3 | Dhaene, G. and K. Jochmans (2015) Split-panel jackknife estimation of fixed-effect models | 0.644 | 2 | 2 | 100% |
| 4 | Fan, J., T.-C. Hu, and Y. K. Truong (1994) Robust non-parametric function estimation | 0.644 | 2 | 2 | 100% |
| 5 | Fernández-Val, I. and M. Weidner (2018) Fixed effects estimation of large-T panel data models | 0.644 | 2 | 2 | 100% |
| 6 | Hahn, J. and W. Newey (2004) Jackknife and analytical bias reduction for nonlinear panel models | 0.644 | 2 | 2 | 100% |
| 7 | Horowitz, J. L (1998) Bootstrap methods for median regression models | 0.511 | 2 | 2 | 50% |
| 8 | Chernozhukov, V., I. Fernández-Val, J. Hahn, and W. Newey (2013) Average and quantile effects in nonseparable panel models | 0.511 | 2 | 1 | 100% |
| 9 | Evdokimov, K (2010) Identification and estimation of a nonparametric panel data model with unobserved heterogeneity | 0.511 | 2 | 1 | 100% |
| 10 | Altonji, J. G. and R. L. Matzkin (2005) Cross section and panel data estimators for nonseparable models with endogenous regressors | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure | 0.843 | 5 | 3 |