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Tracking world trade and GDP in real time

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  • Roberto Golinelli

    () (University of Bologna)

  • Giuseppe Parigi

    () (Bank of Italy)

Abstract

This paper proposes a simple procedure to obtain monthly assessments of short-run perspectives for quarterly world GDP and trade. It combines emerging and advanced countries� high frequency information to explain quarterly national accounts variables through bridge models. The union of all bridge equations leads to our world bridge model (WBM). The WBM econometric approach is new for two reasons: its equations combine traditional short-run bridging with theoretical level-relationships; it is the first time that forecasts of world GDP and trade are computed for advanced and emerging countries on the basis of a real-time database of 7,000 time series. Although the performance of the equations that are automatically searched for should be taken as a lower bound, results show a better WBM forecasting ability than the benchmark case and confirm the usefulness of combining WBM real-time forecasts with preliminary releases to improve the prediction of world trade. Finally, we show that the (unrealistic) use of revised data leads to a systematic overstatement of model forecasting performance.

Suggested Citation

  • Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:wptemi:td_920_13
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    Citations

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    Cited by:

    1. L. Ferrara & C. Marsilli, 2014. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Working papers 515, Banque de France.
    2. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
    3. 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.
    4. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    5. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    6. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    7. Ravazzolo, Francesco & Vespignani, Joaquin, 2017. "World steel production: A new monthly indicator of global real economic activity," Working Papers 2017-08, University of Tasmania, Tasmanian School of Business and Economics.
    8. Sokolov-Mladenović, Svetlana & Milovančević, Milos & Mladenović, Igor, 2017. "Evaluation of trade influence on economic growth rate by computational intelligence approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 358-362.
    9. repec:col:000152:015779 is not listed on IDEAS

    More about this item

    Keywords

    world trade and GDP forecasts; augmented bridge models; real-time data; forecasting ability;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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