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A Canonical Correlation Approach for Selecting the Number of Dynamic Factors

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  • Jörg Breitung
  • Uta Pigorsch

Abstract

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Suggested Citation

  • Jörg Breitung & Uta Pigorsch, 2013. "A Canonical Correlation Approach for Selecting the Number of Dynamic Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 23-36, February.
    • Handle: RePEc:bla:obuest:v:75:y:2013:i:1:p:23-36
    DOI: obes.12003
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    Citations

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    Cited by:

    1. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    2. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    3. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2016. "Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem," Journal of Econometrics, Elsevier, vol. 192(1), pages 190-206.
    4. Xu Cheng & Zhipeng Liao & Frank Schorfheide, 2016. "Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1511-1543.
    5. Joakim Westerlund & Sagarika Mishra, 2017. "On the determination of the number of factors using information criteria with data-driven penalty," Statistical Papers, Springer, vol. 58(1), pages 161-184, March.
    6. Lütkepohl, Helmut, 2014. "Structural vector autoregressive analysis in a data rich environment: A survey," SFB 649 Discussion Papers 2014-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    8. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    9. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.
    10. Jörg Breitung & In Choi, 2013. "Factor models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 11, pages 249-265, Edward Elgar Publishing.
      • In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2011.
    11. repec:dgr:rugsom:14008-eef is not listed on IDEAS
    12. Breitung, Jörg & Eickmeier, Sandra, 2014. "Analyzing business and financial cycles using multi-level factor models," Discussion Papers 11/2014, Deutsche Bundesbank.
    13. Otter, Pieter W. & Jacobs, Jan P.A.M. & Reijer, Ard H.J. de, 2014. "A criterion for the number of factors in a data-rich environment," Research Report 14008-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    14. Alexander Chudik & M. Hashem Pesaran, 2013. "Large Panel Data Models with Cross-Sectional Dependence: A Survey," CESifo Working Paper Series 4371, CESifo.
    15. repec:hum:wpaper:sfb649dp2014-004 is not listed on IDEAS
    16. Kappler, Marcus & Schleer, Frauke, 2017. "A financially stressed euro area," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-37.
    17. Hande Karabiyik & Joakim Westerlund, 2021. "Forecasting using cross-section average–augmented time series regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 315-333.
    18. Gelfer, Sacha, 2020. "The effects of professional forecast dissemination on macroeconomic volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 131-156.
    19. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    20. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2014. "Dynamic factor models: A review of the literature," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 73-107.
    21. Kappler, Marcus & Schleer, Frauke, 2013. "How many factors and shocks cause financial stress?," ZEW Discussion Papers 13-100, ZEW - Leibniz Centre for European Economic Research.
    22. Zhao Zhao & Guowei Cui & Shaoping Wang, 2017. "A Monte Carlo comparison of estimating the number of dynamic factors," Empirical Economics, Springer, vol. 53(3), pages 1217-1241, November.

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