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Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators

Author

Listed:
  • Katerina Arnostova
  • David Havrlant
  • Lubos Ruzicka
  • Peter Toth

Abstract

We evaluate the out-of-sample forecasting performance of six competing models at horizons of up to three quarters ahead in a pseudo-real time setup. All the models use information in monthly indicators released ahead of quarterly GDP. We estimate two models – averaged vector autoregressions and bridge equations – relying on just a few monthly indicators. The remaining four models condition the forecast on a large set of monthly series. These models comprise two standard principal components models, a dynamic factor model based on the Kalman smoother and a generalized dynamic factor model. We benchmark our results to the performance of a naïve model and the historical near-term forecasts of the Czech National Bank’s staff. The findings are also compared with a related study conducted by ECB staff (Barhoumi et al., 2008). In the Czech case, standard principal components is the most precise model overall up to three quarters ahead. However, the CNB staff’s historical forecasts were the most accurate one quarter ahead.

Suggested Citation

  • Katerina Arnostova & David Havrlant & Lubos Ruzicka & Peter Toth, 2010. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Working Papers 2010/12, Czech National Bank, Research Department.
  • Handle: RePEc:cnb:wpaper:2010/12
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    References listed on IDEAS

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    Cited by:

    1. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP
      [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]
      ," MPRA Paper 63713, University Library of Munich, Germany.
    2. William A. Barnett & Biyan Tang, 2016. "Chinese Divisia Monetary Index and GDP Nowcasting," Open Economies Review, Springer, vol. 27(5), pages 825-849, November.
    3. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    4. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank, Research Department.
    5. Martin Feldkircher & Florian Huber & Josef Schreiner & Marcel Tirpák & Peter Tóth & Julia Wörz, 2015. "Bridging the information gap: small-scale nowcasting models of GDP growth for selected CESEE countries," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 56-75.
    6. Alain Kabundi & Elmarie Nel & Franz Ruch, 2016. "Working Paper – WP/16/01- Nowcasting Real GDP growth in South Africa," Papers 7068, South African Reserve Bank.
    7. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    8. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    9. Hamid Baghestani & Liliana Danila, 2014. "Interest Rate and Exchange Rate Forecasting in the Czech Republic: Do Analysts Know Better than a Random Walk?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(4), pages 282-295, September.
    10. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, Elsevier.
    11. David Havrlant & Peter Tóth & Julia Wörz, 2016. "On the optimal number of indicators – nowcasting GDP growth in CESEE," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 54-72.
    12. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    13. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    14. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    15. repec:eee:ecmode:v:69:y:2018:i:c:p:160-168 is not listed on IDEAS
    16. repec:bof:bofitp:urn:nbn:fi:bof-201506091268 is not listed on IDEAS
    17. Kamil Galuscak & Adam Gersl & Marcela Gronychova & Petr Hlavac & Petr Jakubik & Lubos Komarek & Zlatuse Komarkova & Tomas Konecny & Jakub Seidler, 2014. "Stress-Testing Analyses of the Czech Financial System," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 1, volume 12, number rb12/1 edited by Jan Babecky & Roman Horvath.
    18. Roman Horvath, 2012. "Do Confidence Indicators Help Predict Economic Activity? The Case of the Czech Republic," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(5), pages 398-412, November.
    19. Katerina Arnostova & Jozef Barunik & Jan Filacek & Michal Franta & David Havrlant & Roman Horvath & Filip Novotny & Marie Rakova & Lubos Ruzicka & Branislav Saxa & Katerina Smidkova & Peter Toth, 2012. "Macroeconomic Forecasting: Methods, Accuracy and Coordination," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 1, volume 10, number rb10/1 edited by Jan Babecky.
    20. Oxana Babecka Kucharcukova & Alexis Derviz & Vaclav Hausenblas & Michal Hlavacek & Mark Joy & Narcisa Kadlcakova & Lubos Komarek & Zlatuse Komarkova & Tomas Konecny & Ivana Kubicova & Jitka Lesanovska, 2014. "Macroprudential Research: Selected Issues," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 12, number rb12/2 edited by Jan Babecky & Borek Vasicek.
    21. Robert Ambrisko & Vitezslav Augusta & Jan Babecky & Michal Franta & Dana Hajkova & Petr Kral & Jan Libich & Pavla Netusilova & Milan Rikovsky & Jakub Rysanek & Pavel Soukup & Petr Stehlik & Vilem Vale, 2013. "Macroeconomic Effects of Fiscal Policy," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 11, number rb11/2 edited by Jan Babecky & Kamil Galuscak.
    22. Jaromir Baxa & Michal Franta & Tomas Havranek & Roman Horvath & Miroslav Plasil & Marek Rusnak & Borek Vasicek, 2013. "Transmission of Monetary Policy," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 1, volume 11, number rb11/1 edited by Jan Babecky & Roman Horvath.
    23. Modugno, Michele & Soybilgen, Barış & Yazgan, Ege, 2016. "Nowcasting Turkish GDP and news decomposition," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1369-1384.

    More about this item

    Keywords

    Bridge models; dynamic factor models; GDP forecasting; principal components; real-time evaluation.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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