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Model averaging with covariates that are missing completely at random

Author

Listed:
  • Zhang, Xinyu

Abstract

Missing data is a common problem in economics studies. We propose using Mallows model averaging (MMA) to deal with this problem, which has an important advantage over its competitors in that it asymptotically achieves the lowest possible squared error. A simulation study in comparison with existing methods strongly favors the MMA estimator.

Suggested Citation

  • Zhang, Xinyu, 2013. "Model averaging with covariates that are missing completely at random," Economics Letters, Elsevier, vol. 121(3), pages 360-363.
  • Handle: RePEc:eee:ecolet:v:121:y:2013:i:3:p:360-363
    DOI: 10.1016/j.econlet.2013.09.008
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    References listed on IDEAS

    as
    1. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    2. repec:hal:journl:peer-00815561 is not listed on IDEAS
    3. Schomaker, Michael & Wan, Alan T.K. & Heumann, Christian, 2010. "Frequentist Model Averaging with missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3336-3347, December.
    4. repec:taf:jnlbes:v:30:y:2012:i:1:p:132-142 is not listed on IDEAS
    5. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
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    Citations

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    Cited by:

    1. Zhao, Shangwei & Zhou, Jianhong & Li, Hongjun, 2016. "Model averaging with high-dimensional dependent data," Economics Letters, Elsevier, vol. 148(C), pages 68-71.

    More about this item

    Keywords

    Asymptotic optimality; Mallows model averaging; Missing data;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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