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Model averaging with high-dimensional dependent data

Author

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  • Zhao, Shangwei
  • Zhou, Jianhong
  • Li, Hongjun

Abstract

The past two decades witnessed a prosperous literature on model averaging, however, few authors have examined model averaging under high-dimensional data setting. An exception is~ Ando and Li (2014), which proposed a model averaging procedure to improve prediction accuracy under high-dimensional independent data setting. In this paper, we broaden Ando and Li’s scope of analysis to allow dependent data. We show that under the dependent data setting, their model averaging estimator is still asymptotically optimal. Simulation study demonstrates the finite sample performance of the estimator in a variety of dependent data settings.

Suggested Citation

  • Zhao, Shangwei & Zhou, Jianhong & Li, Hongjun, 2016. "Model averaging with high-dimensional dependent data," Economics Letters, Elsevier, vol. 148(C), pages 68-71.
    • Handle: RePEc:eee:ecolet:v:148:y:2016:i:c:p:68-71
    DOI: 10.1016/j.econlet.2016.09.010
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    References listed on IDEAS

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    1. Gao, Yan & Zhang, Xinyu & Wang, Shouyang & Zou, Guohua, 2016. "Model averaging based on leave-subject-out cross-validation," Journal of Econometrics, Elsevier, vol. 192(1), pages 139-151.
    2. Hjort, Nils Lid & Claeskens, Gerda, 2006. "Focused Information Criteria and Model Averaging for the Cox Hazard Regression Model," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1449-1464, December.
    3. Qingfeng Liu & Ryo Okui, 2013. "Heteroscedasticity‐robust C(p) model averaging," Econometrics Journal, Royal Economic Society, vol. 16(3), pages 463-472, October.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Shangwei Zhao, 2014. "Model averaging based on James–Stein estimators," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(8), pages 1013-1022, November.
    6. Tomohiro Ando & Ker-Chau Li, 2014. "A Model-Averaging Approach for High-Dimensional Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 254-265, March.
    7. Zhao, Shangwei & Zhang, Xinyu & Gao, Yichen, 2016. "Model averaging with averaging covariance matrix," Economics Letters, Elsevier, vol. 145(C), pages 214-217.
    8. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    9. Zhang, Xinyu, 2013. "Model averaging with covariates that are missing completely at random," Economics Letters, Elsevier, vol. 121(3), pages 360-363.
    10. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
    11. Hansen, Bruce E. & Racine, Jeffrey S., 2012. "Jackknife model averaging," Journal of Econometrics, Elsevier, vol. 167(1), pages 38-46.
    12. 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.
    13. Xinyu Zhang & Alan Wan & Sherry Zhou, 2012. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142.
    14. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    15. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
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    More about this item

    Keywords

    Dependent data; High dimension; Model averaging; Optimality;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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