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Counting on count data models : Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data - Zurich Open Repository and Archive


Winkelmann, Rainer (2015). Counting on count data models : Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data. IZA world of labor, 148:online.

Abstract

Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples.

Abstract

Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Poisson regression, negative binomial distribution, zero-inflation, hurdle model
Language:English
Date:May 2015
Deposited On:06 May 2015 15:15
Last Modified:05 Apr 2016 19:14
Publisher:Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA)
Series Name:IZA World of Labor
Number of Pages:10
ISSN:2054-9571
Publisher DOI:https://doi.org/10.15185/izawol.148
Official URL:http://wol.iza.org/articles/counting-on-count-data-models-1.pdf
Related URLs:http://dx.doi.org/10.5167/uzh-110642

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