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Should quarterly government finance statistics be used for fiscal surveillane in Europe?

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  • Pérez, Javier J.
  • Pedregal, Diego J.

Abstract

We use a newly available dataset of euro area quarterly national accounts fiscal data and construct multi-variate, state-space mixed-frequencies models for the government deficit, revenue and expenditure in order to assess its information content and its potential use for fiscal forecasting and monitoring purposes. The models are estimated with annual and quarterly national accounts fiscal data, but also incorporate monthly information taken from the cash accounts of the governments. The results show the usefulness of our approach for real-time fiscal policy surveillance in Europe, given the current policy framework in which the relevant official figures are expressed in annual terms. JEL Classification: C53, E6, H6

Suggested Citation

  • Pérez, Javier J. & Pedregal, Diego J., 2008. "Should quarterly government finance statistics be used for fiscal surveillane in Europe?," Working Paper Series 937, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:2008937
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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Ángel Estrada & José Luis Fernández & Esther Moral & Ana V. Regil, 2004. "A quarterly macroeconometric model of the Spanish Economy," Working Papers 0413, Banco de España;Working Papers Homepage.
    3. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    4. Luís Gordo Mora & João Nogueira Martins, 2007. "How reliable are the statistics for the Stability and Growth Pact?," European Economy - Economic Papers 2008 - 2015 273, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    5. Filip Keereman, 1999. "The track record of the Commission forecasts," European Economy - Economic Papers 2008 - 2015 137, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Michael Artis & Massimiliano Marcellino, 2001. "Fiscal forecasting: The track record of the IMF, OECD and EC," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 20-36.
    8. Onorante, Luca & Pedregal, Diego J. & Pérez, Javier J. & Signorini, Sara, 2010. "The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area," Journal of Policy Modeling, Elsevier, vol. 32(1), pages 98-119, January.
    9. Andrew Harvey & Chia-Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
    10. Gonzalo Camba-Mendez & Ana Lamo, 2004. "Short-term monitoring of fiscal policy discipline," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(2), pages 247-265.
    11. Liu, H & Hall, Stephen G, 2001. "Creating High-Frequency National Accounts with State-Space Modelling: A Monte Carlo Experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 441-449, September.
    12. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    13. Tommaso Proietti & Filippo Moauro, 2006. "Dynamic factor analysis with non-linear temporal aggregation constraints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 281-300.
    14. Namwon Hyung & Clive W.J. Granger, 2008. "Linking series generated at different frequencies This work is part of a PhD dissertation presented at the University of California, San Diego (1999)," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 95-108.
    15. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 214-234, July.
    16. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    17. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    18. Perez, Javier J., 2007. "Leading indicators for euro area government deficits," International Journal of Forecasting, Elsevier, vol. 23(2), pages 259-275.
    19. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    20. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
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    Citations

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    Cited by:

    1. Teresa Leal & Javier J. Pérez & Mika Tujula & Jean-Pierre Vidal, 2008. "Fiscal Forecasting: Lessons from the Literature and Challenges," Fiscal Studies, Institute for Fiscal Studies, vol. 29(3), pages 347-386, September.
    2. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    3. Onorante, Luca & Pedregal, Diego J. & Pérez, Javier J. & Signorini, Sara, 2010. "The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area," Journal of Policy Modeling, Elsevier, vol. 32(1), pages 98-119, January.
    4. Carlos P. Barros & Luis A. Gil-Alana, 2013. "Inflation Forecasting in Angola: A Fractional Approach," African Development Review, African Development Bank, vol. 25(1), pages 91-104, March.
    5. repec:eee:intfor:v:33:y:2017:i:3:p:694-706 is not listed on IDEAS
    6. Giuseppe Bianchi & Tatiana Cesaroni & Ottavio Ricchi, 2015. "ISBEM: An econometric model for the Italian State Budget Expenditures," Working Papers LuissLab 15120, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    7. Giuseppe Bianchi & Tatiana Cesaroni & Ottavio Ricchi, 2013. "Previsioni delle spese del bilancio dello Stato attraverso i flussi di contabilità finanziaria," Rivista di Politica Economica, SIPI Spa, issue 1, pages 271-326, January-M.
    8. Teresa Leal Linares & Javier J. Pérez, 2009. "Un sistema ARIMA con agregación temporal para la previsión y el seguimiento del déficit del Estado," Hacienda Pública Española, IEF, vol. 190(3), pages 27-58, June.
    9. Paredes-Lodeiro, Joan & Pérez, Javier J & Pérez-Quirós, Gabriel, 2015. "Fiscal targets. A guide to forecasters?," CEPR Discussion Papers 10553, C.E.P.R. Discussion Papers.
    10. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    11. Diego J. Pedregal & Javier J. Pérez & Antonio Sánchez Fuentes, 2014. "A Tookit to strengthen Government," Hacienda Pública Española, IEF, vol. 211(4), pages 117-146, December.

    More about this item

    Keywords

    fiscal policies; forecasting; mixed frequency data; unobserved components;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • H6 - Public Economics - - National Budget, Deficit, and Debt

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