Tobacco spending in Georgia: Machine learning approach

Maksym Obrizan, Karine Torosyan () and Norberto Pignatti
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Karine Torosyan: International School of Economics at TSU

No 3184, Working Papers from Research Consulting and Development

Abstract: The purpose of this study is to analyze tobacco spending in Georgia using various machine learning methods applied to a sample of 10,757 households from Integrated Household Survey collected by GeoStat in 2016. Previous research has shown that smoking is the leading cause of death for 35-69 year olds. In addition, tobacco expenditures may constitute as much as 17% of the household budget. Five different algorithms (ordinary least squares, random forest, two gradient boosting methods and deep learning) were applied to 8,173 households (or 76.0%) in the train set. Out-of-sample predictions were then obtained for 2,584 remaining households in the test set. Under the default settings random forest algorithm showed the best performance with more than 10% improvement in terms of root-mean-square error (RMSE). Improved accuracy and availability of machine learning tools in R calls for active use of these methods by policy makers and scientists in health economics, public health and related fields.

Keywords: Tobacco Spending; Household Survey; Georgia; Machine Learning (search for similar items in EconPapers)
JEL-codes: D12 I12 L66 (search for similar items in EconPapers)
Pages: 7 pages
Date: 2018-05
New Economics Papers: this item is included in nep-agr, nep-big, nep-cis, nep-cmp, nep-cwa and nep-hea
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Citations: View citations in EconPapers (1)

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http://rcd.org.ua/RePEc/files/WP3184.pdf First version, 2018 (application/pdf)

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