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Forecasting GDP with global components. This time is different

Author

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  • Hilde C. Bjornland
  • Francesco Ravazzolo
  • Leif Anders Thorsrud

Abstract

We examine whether knowledge of in-sample co-movement across countries can be used in a more systematic way to improve forecast accuracy at the national level. In particular, we ask if a model with common international business cycle factors adds marginal predictive power compared to a domestic alternative? To answer this question we use a Dynamic Factor Model (DFM) and run an out-of-sample forecasting experiment. Our results show that exploiting the informational content in a common global business cycle factor improves forecast accuracy in terms of both point and density forecast evaluation across a large panel of countries. We also document that the Great Recession has a huge impact on this result, causing a clear preference shift towards the model including a common global factor. However, this time is different also in other respects. On longer forecasting horizons the performance of the DFM deteriorates substantially in the aftermath of the Great Recession.

Suggested Citation

  • Hilde C. Bjornland & Francesco Ravazzolo & Leif Anders Thorsrud, 2016. "Forecasting GDP with global components. This time is different," CAMA Working Papers 2016-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2016-26
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    Cited by:

    1. Hilde C. Bj⊘rnland & Leif Anders Thorsrud & Sepideh Khayati Zahiri, 2020. "Do Central Banks Respond Timely to Developments in the Global Economy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(2), pages 285-310, April.
    2. Bjørnland, Hilde C. & Thorsrud, Leif Anders & Torvik, Ragnar, 2019. "Dutch disease dynamics reconsidered," European Economic Review, Elsevier, vol. 119(C), pages 411-433.
    3. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    4. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
    5. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    6. Francesco Ravazzolo & Joaquin L. Vespignani, 2015. "A new monthly indicator of global real economic activity," Globalization Institute Working Papers 244, Federal Reserve Bank of Dallas.
    7. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    8. Servén, Luis & Abate, Girum Dagnachew, 2020. "Adding space to the international business cycle," Journal of Macroeconomics, Elsevier, vol. 65(C).
    9. Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019. "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, vol. 35(2), pages 616-633.
    10. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    11. González-Rivera, Gloria & Maldonado, Javier & Ruiz, Esther, 2019. "Growth in stress," International Journal of Forecasting, Elsevier, vol. 35(3), pages 948-966.
    12. Qingwen Li & Guangxi Yan & Chengming Yu, 2022. "A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    13. Håvard Hungnes, 2018. "Encompassing tests for evaluating multi-step system forecasts invariant to linear transformations," Discussion Papers 871, Statistics Norway, Research Department.
    14. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    15. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    16. Schnücker, A.M., 2019. "Penalized Estimation of Panel Vector Autoregressive Models," Econometric Institute Research Papers EI-2019-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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    More about this item

    Keywords

    Bayesian Dynamic Factor Model (BDFM); forecasting; model uncertainty and global factors;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation

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