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Influence Diagnostics and Estimation Algorithms for Powell's SCLS

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  • Santos Silva, J M C

Abstract

This article studies influence diagnostics and estimation algorithms for Powell's symmetrically censored least squares estimator. The proposed measures of influence are based on one-step approximations to the analogous deletion diagnostics used in least squares regression and can be conveniently constructed using a Newton-type algorithm. Additionally, it is found that this algorithm can be used to substantially reduce the computational burden of the estimator. The results of the article are illustrated with an application.

Suggested Citation

  • Santos Silva, J M C, 2001. "Influence Diagnostics and Estimation Algorithms for Powell's SCLS," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 55-62, January.
  • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:1:p:55-62
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    Cited by:

    1. Tao Chen & Gautam Tripathi, 2014. "A simple consistent test of conditional symmetry in symmetrically trimmed tobit models," CREA Discussion Paper Series 14-04, Center for Research in Economic Analysis, University of Luxembourg.
    2. Leandro M. Magnusson, 2010. "Inference in limited dependent variable models robust to weak identification," Econometrics Journal, Royal Economic Society, vol. 13(3), pages 56-79, October.
    3. Čížek, Pavel, 2012. "Semiparametric robust estimation of truncated and censored regression models," Journal of Econometrics, Elsevier, vol. 168(2), pages 347-366.
    4. Chen, Tao & Tripathi, Gautam, 2017. "A simple consistent test of conditional symmetry in symmetrically trimmed tobit models," Journal of Econometrics, Elsevier, vol. 198(1), pages 29-40.

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