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Forecasting regional GDP with factor models: How useful are national and international data?

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  • Kopoin, Alexandre
  • Moran, Kevin
  • Paré, Jean-Pierre

Abstract

We assess the contribution of national (country-wide) and international data to the task of forecasting the real GDP of Canadian provinces. Using the targeting predictors approach of Bai and Ng (2008) [Bai, J., Ng, S., 2008. Forecasting economic time series using targeted predictors. Journal of Econometrics 146, 304–317], we find that larger datasets containing regional, national and international data help improve forecasting accuracy for horizons below the one-year-ahead mark, but that beyond that horizon, relying on provincial data alone produces the best forecasts. These results suggest that shocks originating at the national and international levels are transmitted to Canadian regions and reflected in regional timeseries fairly rapidly.

Suggested Citation

  • Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.
    • Handle: RePEc:eee:ecolet:v:121:y:2013:i:2:p:267-270
    DOI: 10.1016/j.econlet.2013.08.007
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    2. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    3. V.V. Gamukin & O.S. Miroshnichenko, 2021. "Impact of the Gross Regional Product and Total Monetary Income of the Population on Savings Behavior in the Regions of Russia," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(3), pages 383-405.
    4. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    5. Kopoin, Alexandre, 2015. "Cross-border Banking, Spillover Effects and International Business Cycles," MPRA Paper 65515, University Library of Munich, Germany, revised 06 Apr 2015.
    6. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    7. Valerij Gamukin, 2017. "Structural Change of Gross Regional Product in the Subjects of Ural Federal District," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 410-421.
    8. Yan Shao & Zhe Yang & Tianjian Yang, 2023. "The Road of Post-Industrialization Transformation in Developing Countries Based on Weighted Markov and Grey Correlation Theory, Taking the Change of Industrial Structure in Heilongjiang Province of Ch," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    9. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
    10. Concha Artola & María Gil & Javier J. Pérez & Alberto Urtasun & Alejandro Fiorito & Diego Vila, 2018. "Monitoring the Spanish economy from a regional perspective: main elements of analysis," Occasional Papers 1809, Banco de España.
    11. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    12. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
    13. Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021. "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, vol. 14(2), pages 111-128, August.
    14. Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
    15. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    16. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    17. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    18. V. Gamukin V. & В. Гамукин В., 2018. "Управление структурой валового регионального продукта в субъектах Южного федерального округа // Managing the Gross Regional Product Structure in the Territorial Subjects of the Southern Federal Distri," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 8(2), pages 18-29.

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    More about this item

    Keywords

    Dynamic factor models; Targeted predictors; Regional forecasts;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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