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Automatic identification of general vector error correction models

Listed author(s):
  • Arbués, Ignacio
  • Ledo, Ramiro
  • Matilla-García, Mariano

There are a number of econometrics tools to deal with the different types of situations in which cointegration can appear: I(1), I(2), seasonal, polyno- mial, etc. There are also different kinds of Vector Error Correction models related to these situations. The authors propose a unified theoretical and practical framework to deal with many of these situations. To this aim: (i) they introduce a general class of models and (ii) provide an automatic method to identify models, based on estimating the Smith form of an autoregressive model. Their simulations suggest the power of the new proposed methodology. An empirical example illustrates the methodology.

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File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2016-26
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File URL: https://www.econstor.eu/bitstream/10419/147300/1/871658143.pdf
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Article provided by Kiel Institute for the World Economy (IfW) in its journal Economics: The Open-Access, Open-Assessment E-Journal.

Volume (Year): 10 (2016)
Issue (Month): ()
Pages: 1-41

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Handle: RePEc:zbw:ifweej:201626
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  1. Franchi, Massimo, 2007. "The Integration Order Of Vector Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 23(03), pages 546-553, June.
  2. Paruolo, Paolo, 1996. "On the determination of integration indices in I(2) systems," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 313-356.
  3. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
  4. C. Worthington, Andrew & Higgs, Helen, 2010. "Assessing Financial Integration in the European Union Equity Markets: Panel Unit Root and Multivariate Cointegration and Causality Evidence," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 25, pages 457-479.
  5. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238.
  6. Granger, C W J & Lee, T H, 1989. "Investigation of Production, Sales and Inventory Relationships Using Multicointegration and Non-symmetric Error Correction Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(S), pages 145-159, Supplemen.
  7. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
  8. Lewis, Richard & Reinsel, Gregory C., 1985. "Prediction of multivariate time series by autoregressive model fitting," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 393-411, June.
  9. Massimo Franchi, 2006. "A General Representation Theorem for Integrated Vector Autoregressive Processes," Discussion Papers 06-16, University of Copenhagen. Department of Economics.
  10. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
  11. Tomás Del Barrio Castro & Denise R. Osborn, 2011. "HEGY Tests in the Presence of Moving Averages," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(5), pages 691-704, October.
  12. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  13. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
  14. Haldrup, Niels & Salmon, Mark, 1998. "Representations of I(2) cointegrated systems using the Smith-McMillan form," Journal of Econometrics, Elsevier, vol. 84(2), pages 303-325, June.
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