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Using intra annual information to forecast the annual state deficits : the case of France

Listed author(s):
  • MOULIN, Laurent
  • SALTO, Matteo
  • SILVESTRINI, Andrea
  • VEREDAS, David

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.

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File URL: http://alfresco.uclouvain.be/alfresco/download/attach/workspace/SpacesStore/4ab42b98-80b1-4221-a426-286ed0069148/coredp_2004_48.pdf
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Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2004048.

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Date of creation: 00 Jul 2004
Handle: RePEc:cor:louvco:2004048
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Web page: http://www.uclouvain.be/core
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  1. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-927, July.
  2. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," CORE Discussion Papers 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Palm, F.C. & Nijman, T.E., 1990. "Parameter identification in ARMA-processes in the presence of regular but incomplete sampling," Other publications TiSEM 69e84dde-44ef-4592-93a8-8, Tilburg University, School of Economics and Management.
  4. Palm, Franz C & Nijman, Theo E, 1984. "Missing Observations in the Dynamic Regression Model," Econometrica, Econometric Society, vol. 52(6), pages 1415-1435, November.
  5. Thomas M Fullerton Jr, 2004. "A Composite Approach to Forecasting State Government Revenues," Public Economics 0408006, EconWPA.
  6. Bretschneider, Stuart I. & Gorr, Wilpen L. & Grizzle, Gloria & Klay, Earle, 1989. "Political and organizational influences on the accuracy of forecasting state government revenues," International Journal of Forecasting, Elsevier, vol. 5(3), pages 307-319.
  7. Perez, Javier J., 2007. "Leading indicators for euro area government deficits," International Journal of Forecasting, Elsevier, vol. 23(2), pages 259-275.
  8. 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.
  9. 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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