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Nowcasting GDP Using Available Monthly Indicators

Author

Listed:
  • Davor Kunovac

    (The Croatian National Bank, Croatia)

  • Borna Špalat

Abstract

This paper tests of the extent to which available monthly economic indicators help in nowcasting GDP. For this purpose, a factor model is proposed on data relevant for the movement of the domestic GDP (monthly indicator of real economic activity – MRGA) and its results are compared recursively with models for nowcasting from recent related literature and with simple benchmark models. The evaluation of the results of the model indicates that factor models based on the dynamics of a broad group of variables produce better nowcasts than the benchmark models used. Furthermore, different specifications of factor models have similar performance. Another important finding of the analysis is that it is worthwhile to combine the information available in individual models. In addition to nowcasting, monthly series of GDP growth rates for Croatia based on the movement of a large number of available monthly indicators are also constructed in the paper.

Suggested Citation

  • Davor Kunovac & Borna Špalat, 2014. "Nowcasting GDP Using Available Monthly Indicators," Working Papers 39, The Croatian National Bank, Croatia.
    • Handle: RePEc:hnb:wpaper:39
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    References listed on IDEAS

    as
    1. Michele Modugno & Lucrezia Reichlin & Domenico Giannone & Marta Banbura, 2012. "Nowcasting with Daily Data," 2012 Meeting Papers 555, Society for Economic Dynamics.
    2. Matheson, Troy D., 2010. "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys," Economic Modelling, Elsevier, vol. 27(1), pages 304-314, January.
    3. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    4. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    5. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    6. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    7. 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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    Citations

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    Cited by:

    1. Aleksandra Riedl & Julia Wörz, 2018. "A simple approach to nowcasting GDP growth in CESEE economies," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/18, pages 56-74.
    2. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    3. Antonio Musa, 2022. "Nowcasting Bosnia and Herzegovina GDP in Real Time," IHEID Working Papers 08-2022, Economics Section, The Graduate Institute of International Studies.
    4. Ozana Nadoveza Jelić & Rafael Ravnik, 2021. "Introducing Policy Analysis Croatian MAcroecoNometric Model (PACMAN)," Surveys 41, The Croatian National Bank, Croatia.
    5. Rafael Ravnik & Nikola Bokan, 2018. "Quarterly Projection Model for Croatia," Surveys 34, The Croatian National Bank, Croatia.
    6. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.

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    More about this item

    Keywords

    nowcasting GDP; nowcasting; factor models; Kalman filter;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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