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

Jing Dai (), Stefan Sperlich () and Walter Zucchini ()
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Jing Dai: Universität Kassel
Walter Zucchini: Georg-August Universität

Swiss Journal of Economics and Statistics, 2016, vol. 152, issue 1, 49-80

Abstract: 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.

Keywords: C1; C4; I32; I15; predicting distributions; missing values; household expenditures; income distribution; health economics; impact evaluation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/BF03399422

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