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Forecasting fiscal time series using mixed frequency data

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  • Asimakopoulos, Stylianos
  • Paredes, Joan
  • Warmedinger, Thomas

Abstract

Given the increased importance of …fiscal monitoring, this study amends the existing literature in the …field of intra-annual fi…scal data in two main dimensions. First, we use quarterly fi…scal data to forecast a very disaggregated set of …fiscal series at annual frequency. This makes the analysis useful in the typical forecasting environment of large institutions, which employ a "bottom-up" or disaggregated framework. Aside from this practical type of consideration, we fi…nd that forecasts for total revenues and expenditures via their subcomponents can actually result more accurate than a direct forecast of the aggregate. Second, we employ a Mixed Data Sampling (MiDaS) approach to analyze mixed frequency …fiscal data, which is a methodological novelty. It is shown that MiDaS is the best approach for the analysis of mixed frequency fi…scal data compared to two alternative approaches. The results regarding the information content of quarterly …fiscal data con…rm previous work that such data should be taken into account as it becomes available throughout the year for improving the end-year forecast. For instance, once data for the third quarter is incorporated, the annual forecast becomes very accurate (very close to actual data). We also benchmark against the European Commission’s forecast and fi…nd the results fare favorably, particularly when considering that they stem from a simple univariate framework. JEL Classification: C22, C53, E62, H68

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1550.

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Date of creation: May 2013
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Handle: RePEc:ecb:ecbwps:20131550

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Keywords: aggregated vs disaggregated forecast; Fiscal Policy; Mixed frequency data; short-term forecasting;

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References

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  1. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
  2. Götz Thomas & Hecq Alain & Urbain Jean-Pierre, 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  3. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542, April.
  4. 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 S20-S36.
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  7. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
  8. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  9. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank, Research Centre.
  10. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
  11. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
  12. Onorante, Luca & Pedregal, Diego J. & Pérez, Javier J. & Signorini, Sara, 2008. "The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area," Working Paper Series 0901, European Central Bank.
  13. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  14. repec:fth:eeccco:137 is not listed on IDEAS
  15. Hughes Hallett, Andrew & Kuhn, Moritz & Warmedinger, Thomas, 2010. "The gains from early intervention in Europe: Fiscal surveillance and fiscal planning using cash data," Working Paper Series 1220, European Central Bank.
  16. Joan Paredes & Diego J. Pedregal & Javier J. Pérez, 2009. "A quarterly fiscal database for the euro area based on intra-annual fiscal information," Banco de Espa�a Working Papers 0935, Banco de Espa�a.
  17. Nikita Perevalov & Philipp Maier, 2010. "On the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich Environment," Working Papers 10-10, Bank of Canada.
  18. Filip Keereman, 1999. "The track record of the Commission forecasts," European Economy - Economic Papers 137, Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission.
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Cited by:
  1. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
  2. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.

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