Journal of Business Cycle Research

, Volume 12, Issue 2, pp 165–185 | Cite as

Forecasting Czech GDP Using Mixed-Frequency Data Models

  • Michal Franta
  • David Havrlant
  • Marek Rusnák
Research Paper


In this paper we use a battery of various mixed-frequency data models to forecast Czech GDP growth. The models employed are mixed-frequency vector autoregressions, mixed-data sampling models, and the dynamic factor model. Using a dataset of historical vintages of unrevised macroeconomic and financial data, we evaluate the performance of these models over the 2005–2014 period and compare them with the Czech National Bank’s macroeconomic forecasts. The results suggest that for shorter forecasting horizons the CNB forecasts outperform forecasts based on the mixed-frequency data models. At longer horizons, mixed-frequency vector autoregressions and the dynamic factor model are able to perform similarly or slightly better than the CNB forecasts. Furthermore, moving away from point forecasts, we also explore the potential of density forecasts from Bayesian mixed-frequency vector autoregressions.


Short-term forecasting Real-time data GDP Mixed-frequency data 

JEL Classification

C53 C82 E52 



We acknowledge support from the Czech National Bank (Project No. B6/13), Rusnák acknowledges support from Grant Agency of Charles University (#888413) and the Grant Agency of Czech Republic (Grant p402/12/G097). We thank Marta Bańbura and Eric Ghysels for sharing parts of their Matlab codes. We also thank Oxana Babecká Kucharčuková, Claudia Foroni, Ana Beatriz Galvão, seminar participants at the Czech National Bank and two anonymous referees for their helpful comments. The views expressed here are those of authors and not necessarily those of the Czech National Bank.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Czech National BankPragueCzech Republic
  2. 2.Institute of Economic StudiesCharles UniversityPragueCzech Republic

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