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Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts

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  • Jos Jansen
  • Xiaowen Jin
  • Jasper de Winter

Abstract

We conduct a systematic comparison of the short-term forecasting abilities of eleven statistical models and professional analysts in a pseudo-real time setting, using a large set of monthly indicators. Our analysis covers the euro area and its five largest countries over the years 1996-2011. We find that summarizing the available monthly information in a few factors is a more promising forecasting strategy than averaging a large number of indicator-based forecasts. The dynamic and static factor model outperform other models, especially during the crisis period. Judgmental forecasts by professional analysts often embody valuable information that could be used to enhance forecasts derived from purely mechanical procedures.

Suggested Citation

  • Jos Jansen & Xiaowen Jin & Jasper de Winter, 2012. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," DNB Working Papers 365, Netherlands Central Bank, Research Department.
  • Handle: RePEc:dnb:dnbwpp:365
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    Cited by:

    1. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and forecasting economic growth in the euro area using principal components," DNB Working Papers 415, Netherlands Central Bank, Research Department.
    2. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    3. repec:eee:intfor:v:33:y:2017:i:4:p:786-800 is not listed on IDEAS
    4. D’Elia Enrico, 2014. "Predictions vs. Preliminary Sample Estimates: The Case of Eurozone Quarterly GDP," Journal of Official Statistics, De Gruyter Open, vol. 30(3), pages 1-22, September.
    5. Jos Jansen & Jasper de Winter, 2016. "Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries," DNB Working Papers 507, Netherlands Central Bank, Research Department.
    6. Hirashima, Ashley & Jones, James & Bonham, Carl S. & Fuleky, Peter, 2017. "Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism," Annals of Tourism Research, Elsevier, vol. 63(C), pages 191-202.
    7. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    8. 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.
    9. repec:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0321-6 is not listed on IDEAS
    10. Kieran Mc Morrow & Werner Roeger & Valerie Vandermeulen, 2017. "Evaluating Medium Term Forecasting Methods and their Implications for EU Output Gap Calculations," European Economy - Discussion Papers 2015 - 070, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    11. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    12. Etienne Farvaque & Florence Huart, 2017. "A policymaker’s guide to a Euro area stabilization fund," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 34(1), pages 11-30, April.
    13. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
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    15. repec:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0337-y is not listed on IDEAS
    16. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.
    17. repec:kap:ecopln:v:50:y:2017:i:3:d:10.1007_s10644-017-9212-7 is not listed on IDEAS
    18. Marković, Dušan & Petković, Dalibor & Nikolić, Vlastimir & Milovančević, Miloš & Petković, Biljana, 2017. "Soft computing prediction of economic growth based in science and technology factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 217-220.

    More about this item

    Keywords

    nowcasting; professional forecasters; factor model; judgment; forecasting;

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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