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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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    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.
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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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