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Principal Components Analysis of Cointegrated Time Series


  • Harris, David


This paper considers the analysis of cointegrated time series using principal components methods. These methods have the advantage of requiring neither the normalization imposed by the triangular error correction model nor the specification of a finite-order vector autoregression. An asymptotically efficient estimator of the cointegrating vectors is given, along with tests forcointegration and tests of certain linear restrictions on the cointegrating vectors. An illustrative application is provided.

Suggested Citation

  • Harris, David, 1997. "Principal Components Analysis of Cointegrated Time Series," Econometric Theory, Cambridge University Press, vol. 13(04), pages 529-557, August.
  • Handle: RePEc:cup:etheor:v:13:y:1997:i:04:p:529-557_00

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    References listed on IDEAS

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    Cited by:

    1. Nielsen, Morten Ørregaard, 2010. "Nonparametric cointegration analysis of fractional systems with unknown integration orders," Journal of Econometrics, Elsevier, vol. 155(2), pages 170-187, April.
    2. Giovanni Urga & Lorenzo Trapani, 2004. "Cointegration versus Spurious Regression in Heterogeneous Panels," Econometric Society 2004 North American Summer Meetings 266, Econometric Society.
    3. Jorg Breitung, 2005. "A Parametric approach to the Estimation of Cointegration Vectors in Panel Data," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 151-173.
    4. Festic, Mejra & Repina, Sebastijan & Volcjak, Robert, 2010. "Estimating Coal Price Dynamics with the Principal Components Method," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 188-212, July.
    5. Snell, Andy, 1998. "Testing for r versus r-1 cointegrating vectors," Journal of Econometrics, Elsevier, vol. 88(1), pages 151-191, November.
    6. Hayashi, Katsuhiko & Kaizoji, Taisei & Pichl, Lukáš, 2007. "Correlation patterns of NIKKEI index constituents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 16-21.
    7. Antzoulatos, Angelos A. & Tsoumas, Chris, 2010. "Financial development and household portfolios - Evidence from Spain, the U.K. and the U.S," Journal of International Money and Finance, Elsevier, vol. 29(2), pages 300-314, March.
    8. Hiroaki Chigira & Taku Yamamoto, 2009. "Forecasting in large cointegrated processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 631-650.
    9. Damiana Giuseppina Costanzo & Damiano Bruno Silipo & Marianna Succurro, 2013. "Over-Indebtedness And Innovation: Some Preliminary Results," Working Papers 201304, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    10. Richard G. Anderson & Hailong Qian & Robert H. Rasche, 2006. "Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators," Working Papers 2006-050, Federal Reserve Bank of St. Louis.
    11. Panagiotis Reppas & Efthymios Tsionas & Dimitris Christopoulos, 2001. "European common stochastic long-run trends," Journal of Economics, Springer, vol. 74(2), pages 119-130, June.
    12. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2006. "Deregulated Wholesale Electricity Prices in Europe," Working Papers 20061001, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    13. Shintani, Mototsugu, 2001. "A simple cointegrating rank test without vector autoregression," Journal of Econometrics, Elsevier, vol. 105(2), pages 337-362, December.
    14. Focardi, Sergio M. & Fabozzi, Frank J. & Mitov, Ivan K., 2016. "A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 134-155.
    15. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2007. "A robust multivariate long run analysis of European electricity prices," Working Papers 20070901, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    16. Breitung, Jorg, 2002. "Nonparametric tests for unit roots and cointegration," Journal of Econometrics, Elsevier, vol. 108(2), pages 343-363, June.
    17. Ahlgren, Niklas & Nyblom, Jukka, 2003. "A General Test for the Cointegrating Rank in Vector Autoregressive Models," Working Papers 499, Hanken School of Economics.
    18. Tilak Abeysinghe & Keen Meng Choy, 2005. "Modelling Small Economy Exports : The Case of Singapore," Trade Working Papers 21980, East Asian Bureau of Economic Research.
    19. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    20. repec:bla:jtsera:v:38:y:2017:i:5:p:711-732 is not listed on IDEAS
    21. Breitung, Jörg, 1998. "Canonical correlation statistics for testing the cointegration rank in a reversed order," SFB 373 Discussion Papers 1998,105, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    22. Dobrescu, Emilian & Gaftea, Viorel & Scutaru, Cornelia, 2010. "Using the Leontief Matrix to Estimate the Impact of Investments upon the Global Output," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 176-187, July.
    23. Pierre Perron & Eduardo Zorita & Francisco Estrada & Pierre Perron, 2017. "Extracting and Analyzing the Warming Trend in Global and Hemispheric Temperatures," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 711-732, September.
    24. Gomez-Biscarri, Javier & Hualde, Javier, 2015. "Regression-based analysis of cointegration systems," Journal of Econometrics, Elsevier, vol. 186(1), pages 32-50.
    25. Javier Fernandez-Macho, 2013. "A Test for the Null of Multiple Cointegrating Vectors," Economics Series Working Papers 657, University of Oxford, Department of Economics.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models


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