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Forecasting fiscal time series using mixed frequency data

Author

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

Abstract

Given the increased importance of fiscal monitoring, this study amends the existing literature in the field of intra-annual fiscal data in two main dimensions. First, we use quarterly fiscal 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 find 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 fiscal data compared to two alternative approaches. The results regarding the information content of quarterly fiscal data confirm 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 find the results fare favorably, particularly when considering that they stem from a simple univariate framework. JEL Classification: C22, C53, E62, H68

Suggested Citation

  • Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20131550
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    2. Michael Funke & Aaron Mehrotra & Hao Yu, 2015. "Tracking Chinese CPI inflation in real time," Empirical Economics, Springer, pages 1619-1641.
    3. 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.
    4. Fungáčová, Zuzana & Herrala, Risto & Weill, Laurent, 2013. "The influence of bank ownership on credit supply: Evidence from the recent financial crisis," Emerging Markets Review, Elsevier, pages 136-147.
    5. Karmakar, Sudipto & Mok, Junghwan, 2015. "Bank capital and lending: An analysis of commercial banks in the United States," Economics Letters, Elsevier, pages 21-24.
    6. Pérez Quirós, Gabriel & Pérez, Javier J. & Paredes, Joan, 2015. "Fiscal targets. A guide to forecasters?," Working Paper Series 1834, European Central Bank.
    7. Diego J. Pedregal & Javier J. Pérez & A. Jesús Sánchez-Fuentes, 2014. "A toolkit to strengthen government budget surveillance," Working Papers 1416, Banco de España;Working Papers Homepage.
    8. Pérez Quirós, Gabriel & Pérez, Javier J. & Paredes, Joan, 2015. "Fiscal targets. A guide to forecasters?," Working Paper Series 1834, European Central Bank.
    9. repec:wly:japmet:v:31:y:2016:i:7:p:1276-1290 is not listed on IDEAS
    10. Funke, Michael & Paetz, Michael, 2013. "Housing prices and the business cycle: An empirical application to Hong Kong," Journal of Housing Economics, Elsevier, pages 62-76.
    11. 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.
    12. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models," Working Papers 035, Centre for Econometric and Allied Research, University of Ibadan.
    13. Diego J. Pedregal & Javier J. Pérez & Antonio Sánchez Fuentes, 2014. "A Tookit to strengthen Government," Hacienda Pública Española, IEF, vol. 211(4), pages 117-146, December.

    More about this item

    Keywords

    aggregated vs. disaggregated forecast; fiscal policy; mixed frequency data; short-term forecasting;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy
    • H68 - Public Economics - - National Budget, Deficit, and Debt - - - Forecasts of Budgets, Deficits, and Debt

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