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

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

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

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Paper provided by European Central Bank in its series Working Paper Series with number 0937.

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Date of creation: Sep 2008
Date of revision:
Handle: RePEc:ecb:ecbwps:20080937
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  1. 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.
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  3. 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.
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  14. 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 441-49, September.
  15. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
  16. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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