Markov Forecasting Methods for Welfare Caseloads / Jeffrey Grogger.

Format:

Book

Author/Creator:

Grogger, Jeffrey.

Contributor:

National Bureau of Economic Research.

Series:

Working Paper Series (National Bureau of Economic Research) no. w11682.

NBER working paper series no. w11682

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2005.

Summary:

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.

Notes:

Print version record

October 2005.