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Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data

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  • Stylianos Asimakopoulos
  • Joan Paredes
  • Thomas Warmedinger

Abstract

The sovereign debt crisis has increased the importance of monitoring budgetary execution. We employ real‐time data using a mixed data sampling (MiDaS) methodology to demonstrate how budgetary slippages can be detected early on. We show that in spite of using real‐time data, the year‐end forecast errors diminish significantly when incorporating intra‐annual information. Our results show the benefits of forecasting aggregates via subcomponents, in this case total government revenue and expenditure. Our methodology could significantly improve fiscal surveillance and could therefore be an important part of the European Commission's model toolkit.

Suggested Citation

  • Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    • Handle: RePEc:bla:scandj:v:122:y:2020:i:1:p:369-390
    DOI: 10.1111/sjoe.12338
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    1. Filip Keereman, 1999. "The track record of the Commission forecasts," European Economy - Economic Papers 2008 - 2015 137, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    2. 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.
    3. 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.
    4. 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.
    5. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 31-67.
    6. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    7. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    8. Jacopo Cimadomo, 2012. "Fiscal Policy in Real Time," Scandinavian Journal of Economics, Wiley Blackwell, vol. 114(2), pages 440-465, June.
    9. 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 20-36.
    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. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    12. Pedregal, Diego J. & Pérez, Javier J., 2010. "Should quarterly government finance statistics be used for fiscal surveillance in Europe?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 794-807, October.
    13. S. Borağan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2‐3), pages 319-340, March.
    14. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    15. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    16. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    17. Helmut Lütkepohl, 2010. "Forecasting Aggregated Time Series Variables: A Survey," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-26.
    18. Nikita Perevalov & Philipp Maier, 2010. "On the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich Environment," Staff Working Papers 10-10, Bank of Canada.
    19. Andrew Hughes Hallett & Moritz Kuhn & Thomas Warmedinger, 2012. "The gains from early intervention in Europe: Fiscal surveillance and fiscal planning using cash data," European Journal of Government and Economics, Europa Grande, vol. 1(1), pages 44-65, June.
    20. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    21. 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.
    22. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    23. Asimakopoulos, Panagiotis & Asimakopoulos, Stylianos & Kourogenis, Nikolaos & Tsiritakis, Emmanuel, 2017. "Time-Disaggregated Dividend–Price Ratio and Dividend Growth Predictability in Large Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(5), pages 2305-2326, October.
    24. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    25. Perez, Javier J., 2007. "Leading indicators for euro area government deficits," International Journal of Forecasting, Elsevier, vol. 23(2), pages 259-275.
    26. 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.
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      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Saidjon Shiralievich Tavarov & Alexander Sidorov & Zsolt Čonka & Murodbek Safaraliev & Pavel Matrenin & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty," Energies, MDPI, vol. 16(8), pages 1-18, April.
    3. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    4. Marcell Göttert & Robert Lehmann, 2021. "Tax Revenue Forecast Errors: Wrong Predictions of the Tax Base or the Elasticity?," CESifo Working Paper Series 9148, CESifo.
    5. Robert Ambrisko, 2022. "Nowcasting Macroeconomic Variables Using High-Frequency Fiscal Data," Working Papers 2022/5, Czech National Bank.
    6. Barbara Batóg & Jacek Batóg, 2021. "Regional Government Revenue Forecasting: Risk Factors of Investment Financing," Risks, MDPI, vol. 9(12), pages 1-15, November.

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