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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Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number
2004048.
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy H60 - Public Economics - - National Budget, Deficit, and Debt - - - General
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