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Forecasting welfare caseloads: The case of the Japanese public assistance program

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  • Hayashi, Masayoshi

Abstract

Forecasting welfare caseloads has grown in importance in Japan because of their recent rapid increase. Given that the forecasting literature on welfare caseloads only focuses on US cases and utilizes limited classes of forecasting models, this study employs multiple alternative methods in order to forecast Japanese welfare caseloads and compare forecasting performances. In pseudo real-time forecasting, VAR and forecast combinations tend to outperform the other methods investigated. In real-time forecasting, however, a simple version of forecast combinations seems to perform better than the remaining models, predicting that welfare caseloads in Japan will surpass 1.7 million by the beginning of 2016, an approximately 20% increase in five years from the beginning of 2011.

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  • Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
  • Handle: RePEc:eee:soceps:v:48:y:2014:i:2:p:105-114
    DOI: 10.1016/j.seps.2013.10.002
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    2. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.

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    More about this item

    Keywords

    Public assistance; Welfare caseload; Forecast combination; Japan;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • H68 - Public Economics - - National Budget, Deficit, and Debt - - - Forecasts of Budgets, Deficits, and Debt
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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