Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
arXiv 1 Jun 2019 · Statistics — Computation · publishedJournal of Statistical Software (2019) · 23 citations (OpenAlex)
arXiv:1906.00198 · PDF · DOI · OpenAlex · Extracted main text
Nonparametric kernel density and local polynomial regression estimators are very popular in Statistics, Economics, and many other disciplines. They are routinely employed in applied work, either as part of the main empirical analysis or as a preliminary ingredient entering some other estimation or inference procedure. This article describes the main methodological and numerical features of the software package nprobust, which offers an array of estimation and inference procedures for nonparametric kernel-based density and local polynomial regression methods, implemented in both the R and Stata statistical platforms. The package includes not only classical bandwidth selection, estimation, and inference methods (Wand and Jones, 1995; Fan and Gijbels, 1996), but also other recent developments in the statistics and econometrics literatures such as robust bias-corrected inference and coverage error optimal bandwidth selection (Calonico, Cattaneo and Farrell, 2018, 2019). Furthermore, this article also proposes a simple way of estimating optimal bandwidths in practice that always delivers the optimal mean square error convergence rate regardless of the specific evaluation point, that is, no matter whether it is implemented at a boundary or interior point. Numerical performance is illustrated using an empirical application and simulated data, where a detailed numerical comparison with other R packages is given.
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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 | Calonico S, Cattaneo MD, Farrell MH (2018) On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference | 1.000 | 19 | 5 | 100% |
| 2 | Fan J, Gijbels I (1996) Local Polynomial Modelling and Its Applications | 1.000 | 5 | 3 | 100% |
| 3 | Calonico S, Cattaneo MD, Farrell MH (2019) Coverage Error Optimal Confidence Intervals for Local Polynomial Regression | 0.874 | 5 | 2 | 100% |
| 4 | Efron B, Feldman D (1991) Compliance as an Explanatory Variable in Clinical Trials | 0.737 | 3 | 2 | 100% |
| 5 | Wand M, Jones M (1995) Kernel Smoothing | 0.511 | 2 | 1 | 100% |
| 6 | Abadie A, Imbens GW (2008) Estimation of the Conditional Variance in Paired Experiments | 0.405 | 1 | 1 | 100% |
| 7 | Cattaneo MD, Crump RK, Farrell MH, Feng Y (2019) Binscatter Regressions | 0.405 | 1 | 1 | 100% |
| 8 | Cattaneo MD, Farrell MH, Feng Y (2019) lspartition: Partitioning-Based Least Squares Regression | 0.405 | 1 | 1 | 100% |
| 9 | Cattaneo MD, Farrell MH (2013) Optimal Convergence Rates, Bahadur Representation, and Asymptotic Normality of Partitioning Estimators | 0.405 | 1 | 1 | 100% |
| 10 | Cattaneo MD, Jansson M, Ma X (2019) lpdensity: Local Polynomial Density Estimation and Inference | 0.405 | 1 | 1 | 100% |
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