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Some Further Results on the Use of Proxy Variables in Prediction

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  • Stahlecker, Peter
  • Trenkler, Gotz

Abstract

In econometric analysis, occasionally some of the regressors are not available. In this paper, the authors study the implications of two strategies: either cancel these regressors from the model or use proxy variables instead. It is analyzed which of both strategies leads to an improvement in conditional prediction of the systematic part in terms of the mean square error criterion. Furthermore, some characterizations for admissibility are given. Copyright 1993 by MIT Press.

Suggested Citation

  • Stahlecker, Peter & Trenkler, Gotz, 1993. "Some Further Results on the Use of Proxy Variables in Prediction," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 707-711, November.
    • Handle: RePEc:tpr:restat:v:75:y:1993:i:4:p:707-11
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    Cited by:

    1. Brutschin, Elina & Fleig, Andreas, 2016. "Innovation in the energy sector – The role of fossil fuels and developing economies," Energy Policy, Elsevier, vol. 97(C), pages 27-38.
    2. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
    3. Charles B. Perkins & J. Christina Wang, 2019. "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers 19-16, Federal Reserve Bank of Boston.

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