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truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

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  • Karlsson, Maria
  • Lindmark, Anita

Abstract

Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and finite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

Suggested Citation

  • Karlsson, Maria & Lindmark, Anita, 2014. "truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i14).
    • Handle: RePEc:jss:jstsof:v:057:i14
    DOI: http://hdl.handle.net/10.18637/jss.v057.i14
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    References listed on IDEAS

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    1. Powell, James L, 1986. "Symmetrically Trimmed Least Squares Estimation for Tobit Models," Econometrica, Econometric Society, vol. 54(6), pages 1435-1460, November.
    2. Karlsson, Maria & Cantoni, Eva & de Luna, Xavier, 2009. "Local polynomial regression with truncated or censored response," Working Paper Series 2009:25, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    3. Paulsen, Jostein & Lunde, Astrid & Skaug, Hans Julius, 2008. "Fitting mixed-effects models when data are left truncated," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 121-133, August.
    4. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    5. Aldrin, Magne, 2006. "Improved predictions penalizing both slope and curvature in additive models," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 267-284, January.
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    1. Y. Andriyana & I. Gijbels & A. Verhasselt, 2018. "Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity," Statistical Papers, Springer, vol. 59(4), pages 1589-1621, December.

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