Markov Forecasting Methods for Welfare Caseloads
AbstractForecasting welfare caseloads, particularly turning points, has become more important than ever. Since welfare reform, welfare has been funded via a block grant, which means that unforeseen changes in caseloads can have important fiscal implications for states. In this paper I develop forecasts based on the theory of Markov chains. Since today's caseload is a function of the past caseload, the caseload exhibits inertia. The method exploits that inertia, basing forecasts of the future caseload on past functions of entry and exit rates. In an application to California welfare data, the method accurately predicted the late-2003 turning point roughly one year in advance.
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Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11682.
Date of creation: Oct 2005
Date of revision:
Publication status: published as Grogger, Jeffrey, 2007. "Markov forecasting methods for welfare caseloads," Children and Youth Services Review, Elsevier, vol. 29(7), pages 900-911, July.
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Other versions of this item:
- I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-15 (All new papers)
- NEP-ECM-2005-10-15 (Econometrics)
- NEP-ETS-2005-10-15 (Econometric Time Series)
- NEP-FOR-2005-10-15 (Forecasting)
- NEP-ICT-2005-10-15 (Information & Communication Technologies)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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