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Partially Adaptive Estimation of the Censored Regression Model

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  • Randall A. Lewis
  • James B. McDonald

Abstract

Data censoring causes ordinary least squares estimates of linear models to be biased and inconsistent. Tobit, semiparametric, and partially adaptive estimators have been considered as possible solutions. This paper proposes several new partially adaptive estimators that cover a wide range of distributional characteristics. A simulation study is used to investigate the estimators' relative efficiency in these settings. The partially adaptive censored regression estimators have little efficiency loss for censored normal errors and may outperform Tobit and semiparametric estimators considered for non-normal distributions. An empirical example of out-of-pocket expenditures for a health insurance plan provides an example, which supports these results.

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  • Randall A. Lewis & James B. McDonald, 2014. "Partially Adaptive Estimation of the Censored Regression Model," Econometric Reviews, Taylor & Francis Journals, vol. 33(7), pages 732-750, October.
    • Handle: RePEc:taf:emetrv:v:33:y:2014:i:7:p:732-750
    DOI: 10.1080/07474938.2012.690691
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    References listed on IDEAS

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    Cited by:

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    2. Kerman, Sean C. & McDonald, James B., 2013. "Skewness–kurtosis bounds for the skewed generalized T and related distributions," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2129-2134.
    3. Harvey, Andew & Liao, Yin, 2023. "Dynamic Tobit models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 72-83.
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    6. Chandra Kiran B. Krishnamurthy & Bengt Kriström, 2016. "Determinants of the Price-Premium for Green Energy: Evidence from an OECD Cross-Section," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(2), pages 173-204, June.

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