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Heteroscedasticity-robust model screening: A useful toolkit for model averaging in big data analytics

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  • Xie, Tian

Abstract

Frequentist model averaging has been demonstrated as an efficient tool to deal with model uncertainty in big data analysis. In contrast with a conventional data set, the number of regressors in a big data set is usually quite large, which leads to a exponential number of potential candidate models. In this paper, we propose a heteroscedasticity-robust model screening (HRMS) method that constructs a candidate model set through an iterative procedure. Our simulation results and empirical exercise with big data analytics demonstrate the superiority of our HRMS method over existing methods.

Suggested Citation

  • Xie, Tian, 2017. "Heteroscedasticity-robust model screening: A useful toolkit for model averaging in big data analytics," Economics Letters, Elsevier, vol. 151(C), pages 119-122.
    • Handle: RePEc:eee:ecolet:v:151:y:2017:i:c:p:119-122
    DOI: 10.1016/j.econlet.2016.12.019
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    References listed on IDEAS

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    4. Steven Lehrer & Tian Xie, 2017. "Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 749-755, December.
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    10. 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.
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    12. Xinyu Zhang & Dalei Yu & Guohua Zou & Hua Liang, 2016. "Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1775-1790, October.
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    Cited by:

    1. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    2. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    3. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    4. Qingfeng Liu & Andrey L. Vasnev, 2019. "A Combination Method for Averaging OLS and GLS Estimators," Econometrics, MDPI, vol. 7(3), pages 1-12, September.
    5. Zhang, Xiaomeng & Zhang, Xinyu, 2023. "Optimal model averaging based on forward-validation," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.

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    More about this item

    Keywords

    Model screening; Model averaging; Big data analytics;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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