arXiv 24 Feb 2018 · Econometrics · publishedEconometrics Journal (2019) · 18 citations (OpenAlex)
arXiv:1802.08825 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the cross-sectional sample size (N) and the time-series length (T). In particular, it makes the condition on N and T stronger and more complicated than those typically observed in the long-panel literature without kernel smoothing. We also consider a split-panel jackknife method to correct bias and construction of confidence intervals. An empirical application and Monte Carlo simulations illustrate our procedure 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 | R. Okui and T. Yanagi (2019) Panel data analysis with heterogeneous dynamics | 0.979 | 16 | 5 | 94% |
| 2 | S. Calonico, M. D. Cattaneo, and M. H. Farrell (2018) On the effect of bias estimation on coverage accuracy in nonparametric inference | 0.965 | 10 | 4 | 90% |
| 3 | K. Jochmans and M. Weidner (2019) Inference on a distribution from noisy draws | 0.874 | 10 | 2 | 100% |
| 4 | G. Dhaene and K. Jochmans (2015) Split-panel jackknife estimation of fixed effects models | 0.874 | 6 | 2 | 100% |
| 5 | M. J. Crucini, M. Shintani, and T. Tsuruga (2015) Noisy information, distance and law of one price dynamics across us cities | 0.874 | 5 | 2 | 100% |
| 6 | Q. Li and J. S. Racine (2007) Nonparametric Econometrics: Theory and Practice | 0.737 | 3 | 3 | 67% |
| 7 | A. F. Galvao and K. Kato (2014) Estimation and inference for linear panel data models under misspecification when both $n$ and $T$ are large | 0.644 | 2 | 2 | 100% |
| 8 | Y.-J. Lee, R. Okui, and M. Shintani (2018) Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes | 0.644 | 2 | 2 | 100% |
| 9 | C. Hsiao, M. H. Pesaran, and A. K. Tahmiscioglu (1999) Bayes estimation of short-run coefficients in dynamic panel data models | 0.585 | 3 | 1 | 100% |
| 10 | J. E. Anderson and E. Van Wincoop (2004) Trade costs | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference on Linear Regressions with Two-Way Unobserved Heterogeneity | 0.585 | 3 | 1 |
| 2 | Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional Heterogeneities | 0.405 | 1 | 1 |