Markov Forecasting Methods for Welfare Caseloads

Jeffrey Grogger

No 11682, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Forecasting 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.

JEL-codes: I3 (search for similar items in EconPapers)
Date: 2005-10
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ict
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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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Journal Article: Markov forecasting methods for welfare caseloads (2007)
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