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A Simple Method for Predicting Distributions by Means of Covariates with Examples from Poverty and Health Economics

Swiss Journal of Economics and Statistics volume 152, pages 49–80 (2016)Cite this article

Summary

We present an integration based procedure for predicting the distribution f of an indicator of interest in situations where, in addition to the sample data, one has access to covariates that are available for the entire population. The proposed method, based on similar ideas that have been used in the literature on policy evaluation, provides an alternative to existing simulation and imputation methods. It is very simple to apply, flexible, requires no additional assumptions, and does not involve the inclusion of artificial random terms. It therefore yields reproducible estimates and allows for valid inference. It also provides a tool for future predictions, scenarios and ex-ante impact evaluation. We illustrate our procedure by predicting income distributions in a case with sample selection, and both current and future doctor visits. We find our approach outperforms other commonly used procedures substantially.

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Authors and Affiliations

  1. Universität Kassel, Nora-Platiel-Str.5, D-34109, Kassel, Switzerland

    Jing Dai

  2. Université de Genève, Geneva School of Economics and Management, Bd du Pont d’Arve 40, CH-1204, Genève, Switzerland

    Stefan Sperlich

  3. Department of Economic Sciences, Georg-August Universität, Platz der Göttinger Sieben 3, D-37073, Göttingen, Switzerland

    Walter Zucchini

Authors
  1. Jing Dai
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  2. Stefan Sperlich
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  3. Walter Zucchini
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Correspondence to Jing Dai.

Additional information

We thank the editors and an anonymous referee for helpful discussion and comments. We also appreciated the discussions with the participants of the Annual meetings of the German Statistical Society 2010, and of the Swiss Statistical Society in 2011.

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Dai, J., Sperlich, S. & Zucchini, W. A Simple Method for Predicting Distributions by Means of Covariates with Examples from Poverty and Health Economics. Swiss J Economics Statistics 152, 49–80 (2016). https://doi.org/10.1007/BF03399422

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  • DOI: https://doi.org/10.1007/BF03399422

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