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Using intra annual information to forecast the annual state deficit. The case of France

Author

Listed:
  • Laurent Moulin
  • Matteo Sala
  • Andrea Silvestrini
  • David Veredas
Abstract
We develop a methodology for using intra-annual data to forecast annual budget deficits. Our approach aims at improving the accuracy of the deficit forecasts, a relevant issue to policy makers in the Eurozone and at proposing a replicable methodology using at best public quantitative information on budgetary data. Using French data on government (State) revenues and expenditures, we estimate intra-annual monthly ARIMA models for all the items of the central government revenues and expenditures. Next, applying temporal aggregation techniques, we infer parameters of the annual models from the estimated parameters of the intra-annual models. These parameters incorporate all the intra-annual information. Finally, we do one period ahead predictions. We are able to update the annual deficit forecast as soon as new monthly data are available. This allows us to detect possible slippages in central government finances.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Laurent Moulin & Matteo Sala & Andrea Silvestrini & David Veredas, 2008. "Using intra annual information to forecast the annual state deficit. The case of France," ULB Institutional Repository 2013/136217, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/136217
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    References listed on IDEAS

    as
    1. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-927, July.
    2. Fullerton, Thomas Jr., 1989. "A composite approach to forecasting state government revenues: Case study of the Idaho sales tax," International Journal of Forecasting, Elsevier, vol. 5(3), pages 373-380.
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    5. 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.
    6. Theo Nijman & Franz Palm, 1990. "Parameter Identification In Arma Processes In The Presence Of Regular But Incomplete Sampling," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(3), pages 239-248, May.
    7. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Palm, Franz C & Nijman, Theo E, 1984. "Missing Observations in the Dynamic Regression Model," Econometrica, Econometric Society, vol. 52(6), pages 1415-1435, November.
    9. Perez, Javier J., 2007. "Leading indicators for euro area government deficits," International Journal of Forecasting, Elsevier, vol. 23(2), pages 259-275.
    10. Nijman, T.E. & Palm, F.C., 1990. "Parameter identification in ARMA processes in the presence of regular but incomplete sampling," Other publications TiSEM 708ee84d-487f-48a4-8169-0, Tilburg University, School of Economics and Management.
    11. Massimiliano Marcellino, 2004. "Forecast Pooling for European Macroeconomic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 91-112, February.
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    Cited by:

    1. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Pérez, Javier J., 2005. "Early-warning tools to forecast general government deficit in the euro area: the role of intra-annual fiscal indicators," Working Paper Series 497, European Central Bank.
    3. 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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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • H60 - Public Economics - - National Budget, Deficit, and Debt - - - General

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