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

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  • Jansen, W. Jos
  • Jin, Xiaowen
  • de Winter, Jasper M.

Abstract

We conduct a systematic comparison of the short-term forecasting abilities of twelve 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 summarizing the available monthly information in a few factors to be a more promising forecasting strategy than averaging a large number of single-indicator-based forecasts. Moreover, it is important to make use of all available monthly observations. The dynamic factor model is the best model overall, particularly for nowcasting and backcasting, due to its ability to incorporate more information (factors). Judgmental forecasts by professional analysts often embody valuable information that could be used to enhance the forecasts derived from purely mechanical procedures.

Suggested Citation

  • Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
  • Handle: RePEc:eee:intfor:v:32:y:2016:i:2:p:411-436
    DOI: 10.1016/j.ijforecast.2015.05.008
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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. 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.
    3. 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.
    4. repec:eee:intfor:v:33:y:2017:i:4:p:786-800 is not listed on IDEAS
    5. repec:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0331-4 is not listed on IDEAS
    6. 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.
    7. 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.
    8. 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.
    9. repec:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0337-y is not listed on IDEAS
    10. 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.
    11. 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.
    12. 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.
    13. repec:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0321-6 is not listed on IDEAS
    14. repec:kap:ecopln:v:50:y:2017:i:3:d:10.1007_s10644-017-9212-7 is not listed on IDEAS
    15. 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.
    16. 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.
    17. 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.
    18. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.

    More about this item

    Keywords

    Forecasting competitions; Factor models; Professional forecasters; Judgment;

    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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