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 28 articles )

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  1. Reese, Timothy; Mojirsheibani, Majid: On the $L_p$ norms of kernel regression estimators for incomplete data with applications to classification (2017)
  2. Kang, Lulu; Joseph, V.Roshan: Kernel approximation: from regression to interpolation (2016)
  3. Maasoumi, Esfandiar; Racine, Jeffrey S.: A solution to aggregation and an application to multidimensional `well-being’ frontiers (2016)
  4. Matousek, Roman; Tzeremes, Nickolaos G.: CEO compensation and bank efficiency: an application of conditional nonparametric frontiers (2016)
  5. Simar, Léopold; Vanhems, Anne; Van Keilegom, Ingrid: Unobserved heterogeneity and endogeneity in nonparametric frontier estimation (2016)
  6. Wanke, Peter; Barros, C.P.; Emrouznejad, Ali: Assessing productive efficiency of banks using integrated fuzzy-DEA and bootstrapping: a case of Mozambican banks (2016)
  7. Baležentis, Tomas; De Witte, Kristof: One- and multi-directional conditional efficiency measurement -- efficiency in Lithuanian family farms (2015)
  8. Northrop, Paul J.: An efficient semiparametric maxima estimator of the extremal index (2015)
  9. Sperlich, Stefan; Theler, Raoul: Modeling heterogeneity: a praise for varying-coefficient models in causal analysis (2015)
  10. Tzeremes, Nickolaos G.: Efficiency dynamics in Indian banking: a conditional directional distance approach (2015)
  11. Daraio, Cinzia; Simar, Léopold: Directional distances and their robust versions: computational and testing issues (2014)
  12. Norets, Andriy; Pelenis, Justinas: Posterior consistency in conditional density estimation by covariate dependent mixtures (2014)
  13. Rao, Marepalli B. (ed.); Rao, C. R. (ed.): Computational statistics with R (2014)
  14. Choi, Taeryon; Woo, Yoonsung: On asymptotic properties of Bayesian partially linear models (2013)
  15. Chu, Ba M.; Huynh, Kim P.; Jacho-Chávez, David T.: Functionals of order statistics and their multivariate concomitants with application to semiparametric estimation by nearest neighbours (2013)
  16. El Ghouch, Anouar; Genton, Marc G.; Bouezmarni, Taoufik: Measuring the discrepancy of a parametric model via local polynomial smoothing (2013)
  17. Taylor, James; Einbeck, Jochen: Challenging the curse of dimensionality in multivariate local linear regression (2013)
  18. Casas, Isabel; Gijbels, Irene: Unstable volatility: the break-preserving local linear estimator (2012)
  19. Marchetti, Stefano; Tzavidis, Nikos; Pratesi, Monica: Non-parametric bootstrap mean squared error estimation for $M$-quantile estimators of small area averages, quantiles and poverty indicators (2012)
  20. Sexton, Joseph; Laake, Petter: Boosted coefficient models (2012)

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