np

Nonparametric Econometrics: The np Package. We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 46 articles )

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  1. Hothorn, Torsten; Möst, Lisa; Bühlmann, Peter: Most likely transformations (2018)
  2. Otneim, Håkon; Tjøstheim, Dag: Conditional density estimation using the local Gaussian correlation (2018)
  3. Abdelaati Daouia and Thibault Laurent and Hohsuk Noh: npbr: A Package for Nonparametric Boundary Regression in R (2017)
  4. Eichner, Gerrit: Kader -- an R package for nonparametric kernel adjusted density estimation and regression (2017)
  5. Marchetti, Stefano; Giusti, Caterina; Salvati, Nicola; Pratesi, Monica: Small area estimation based on M-quantile models in presence of outliers in auxiliary variables (2017)
  6. Marta Sestelo, Nora M. Villanueva, Luis Meira-Machado, Javier Roca-Pardiñas: npregfast: An R Package for Nonparametric Estimation and Inference in Life Sciences (2017)
  7. Norets, Andriy; Pati, Debdeep: Adaptive Bayesian estimation of conditional densities (2017)
  8. Reese, Timothy; Mojirsheibani, Majid: On the $L_p$ norms of kernel regression estimators for incomplete data with applications to classification (2017)
  9. Henderson, Daniel J.; Parmeter, Christopher F.: Teaching nonparametric econometrics to undergraduates (2016)
  10. Kang, Lulu; Joseph, V.Roshan: Kernel approximation: from regression to interpolation (2016)
  11. Maasoumi, Esfandiar; Racine, Jeffrey S.: A solution to aggregation and an application to multidimensional `well-being’ frontiers (2016)
  12. Matousek, Roman; Tzeremes, Nickolaos G.: CEO compensation and bank efficiency: an application of conditional nonparametric frontiers (2016) ioport
  13. Simar, Léopold; Vanhems, Anne; Van Keilegom, Ingrid: Unobserved heterogeneity and endogeneity in nonparametric frontier estimation (2016)
  14. Thomas Nagler: kdecopula: An R Package for the Kernel Estimation of Bivariate Copula Densities (2016) arXiv
  15. Wanke, Peter; Barros, C.P.; Emrouznejad, Ali: Assessing productive efficiency of banks using integrated fuzzy-DEA and bootstrapping: a case of Mozambican banks (2016)
  16. Auray, Stéphane; Klutchnikoff, Nicolas; Rouvière, Laurent: On clustering procedures and nonparametric mixture estimation (2015)
  17. Baležentis, Tomas; De Witte, Kristof: One- and multi-directional conditional efficiency measurement -- efficiency in Lithuanian family farms (2015)
  18. Ferreira, José A.: Some models and methods for the analysis of observational data (2015)
  19. Jenny Häggström and Emma Persson and Ingeborg Waernbaum and Xavier de Luna: CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects (2015)
  20. Northrop, Paul J.: An efficient semiparametric maxima estimator of the extremal index (2015)

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