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Un sistema ARIMA con agregación temporal para la previsión y el seguimiento del déficit del Estado

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Author Info

  • Teresa Leal Linares

    ()
    (Universidad de Huelva)

  • Javier J. Pérez

    ()
    (Banco de España)

Abstract

Our aim is to develop a temporal aggregation ARIMA model to monitor and forecast the annual Spanish central government deficit in order to detect in advance a possible deterioration of the annual public sector balance, using monthly data. We compare the predictive performance of the proposed model with competing forecasting methods, such as annual forecasts directly derived from monthly ARIMA models, and official forecasts published by the government. The results confirm the large improvement in forecasting performance of the aggregated ARIMA system when compared to other alternatives.

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File URL: http://www.ief.es/documentos/recursos/publicaciones/revistas/hac_pub/190_ART_2.pdf
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Bibliographic Info

Article provided by IEF in its journal Hacienda Pública Española/Revista de Economía Pública.

Volume (Year): 190 (2009)
Issue (Month): 3 (June)
Pages: 27-58

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Handle: RePEc:hpe:journl:y:2009:v:190:i:3:p:27-58

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Related research

Keywords: Fiscal forecasting; time series analysis; monitoring; forecasting; public deficit;

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References

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  1. Onorante, Luca & Pedregal, Diego J. & Pérez, Javier J. & Signorini, Sara, 2008. "The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area," Working Paper Series 0901, European Central Bank.
  2. Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
  3. Pedregal, Diego J. & Pérez, Javier J., 2008. "Should quarterly government finance statistics be used for fiscal surveillane in Europe?," Working Paper Series 0937, European Central Bank.
  4. Perez, Javier J., 2007. "Leading indicators for euro area government deficits," International Journal of Forecasting, Elsevier, vol. 23(2), pages 259-275.
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