IDEAS home Printed from https://ideas.repec.org/p/zbw/ifwedp/201633.html
   My bibliography  Save this paper

Automatic identification of general vector error correction models

Author

Listed:
  • Arbués, Ignacio
  • Ledo, Ramiro
  • Matilla-García, Mariano

Abstract

There are a number of econometrics tools to deal with the different type of situations in which cointegration can appear: I(1), I(2), seasonal, polynomial, etc. There are also different kinds of Vector Error Correction models related to these situations. We propose a unified theoretical and practical framework to deal with many of these situations. To this aim: (i) a general class of models is introduced in this paper and (ii) an automatic method to identify models, based on estimating the Smith form of an autoregressive model, is provided. Our simulations suggest the power of the new proposed methodology. An empirical example illustrates the methodology.

Suggested Citation

  • Arbués, Ignacio & Ledo, Ramiro & Matilla-García, Mariano, 2016. "Automatic identification of general vector error correction models," Economics Discussion Papers 2016-33, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwedp:201633
    as

    Download full text from publisher

    File URL: http://www.economics-ejournal.org/economics/discussionpapers/2016-33
    Download Restriction: no
    File URL: https://www.econstor.eu/bitstream/10419/142770/1/862957591.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Franchi, Massimo, 2007. "The Integration Order Of Vector Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 23(3), 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. 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.
    5. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    6. 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.
    7. 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.
    8. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    9. 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.
    10. 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.
    11. 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.
    12. Massimo Franchi, 2006. "A General Representation Theorem for Integrated Vector Autoregressive Processes," Discussion Papers 06-16, University of Copenhagen. Department of Economics.
    13. Yu, Jihai & Lee, Lung-fei, 2010. "Estimation Of Unit Root Spatial Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1332-1362, October.
    14. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    15. 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.
    16. Johansen, Søren, 1992. "A Representation of Vector Autoregressive Processes Integrated of Order 2," Econometric Theory, Cambridge University Press, vol. 8(2), pages 188-202, June.
    17. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Kühl, Michael, 2007. "Cointegration in the foreign exchange market and market efficiency since the introduction of the Euro: Evidence based on bivariate cointegration analyses," University of Göttingen Working Papers in Economics 68, University of Goettingen, Department of Economics.
    3. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
    4. Panayiotis Diamantis & Dimitris Georgoutsos & George Kouretas, 2001. "The Monetary Approach in the Presence of I(2) Components: A Cointegration Analysis of the Official and Black Market for Foreign Currency in Latin America," Working Papers 0108, University of Crete, Department of Economics.
    5. Massimo Franchi & Paolo Paruolo, 2021. "Cointegration, Root Functions and Minimal Bases," Econometrics, MDPI, vol. 9(3), pages 1-27, August.
    6. Committee, Nobel Prize, 2003. "Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity," Nobel Prize in Economics documents 2003-1, Nobel Prize Committee.
    7. Dimitris Georgoutsos & George Kouretas, 2000. "A Multivariate I(2) Cointegration Analysis Of German Hyperinflation," Working Papers 0001, University of Crete, Department of Economics, revised 00 Jul 2001.
    8. Param Silvapulle & Sisira Jayasuriya, 1994. "Testing For Philippines Rice Market Integration: A Multiple Cointegration Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 45(3), pages 369-380, September.
    9. Beare, Brendan K. & Seo, Won-Ki, 2020. "Representation Of I(1) And I(2) Autoregressive Hilbertian Processes," Econometric Theory, Cambridge University Press, vol. 36(5), pages 773-802, October.
    10. William Barnett & Barry E. Jones & Milka Kirova & Travis D. Nesmith & Meenakshi Pasupathy1, 2004. "The Nonlinear Skeletons in the Closet," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 200403, University of Kansas, Department of Economics, revised May 2004.
    11. Lori L. Leachman & Michael Thorpe, 1998. "Intertemporal Solvency in the Small Open Economy of Australia," The Economic Record, The Economic Society of Australia, vol. 74(226), pages 231-242, September.
    12. Carol Alexander & Michael Barrow, 1994. "Seasonality and Cointegration of Regional House Prices in the UK," Urban Studies, Urban Studies Journal Limited, vol. 31(10), pages 1667-1689, December.
    13. Dimitris Georgoutsos & George Kouretas, 2001. "Common Stochastic Trends In International Stock Markets: Testing In An Integrated Framework," Working Papers 0104, University of Crete, Department of Economics.
    14. repec:got:cegedp:68 is not listed on IDEAS
    15. Ankamah-Yeboah, Isaac, 2012. "Spatial Price Transmission in the Regional Maize Markets in Ghana," MPRA Paper 49720, University Library of Munich, Germany.
    16. Sandrine Lardic & Valérie Mignon, 2004. "Robert F. Engle et Clive W.J. Granger prix Nobel d'économie 2003," Revue d'économie politique, Dalloz, vol. 114(1), pages 1-15.
    17. Lütkepohl, Helmut & Saikkonen, Pentti, 1997. "Order selection in testing for the cointegrating rank of a VAR process," SFB 373 Discussion Papers 1997,93, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    18. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    19. Jesus Gonzalo & Tae-Hwy Lee, 2000. "On the robustness of cointegration tests when series are fractionally intergrated," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(7), pages 821-827.
    20. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    21. Jacobson, Tor & Ohlsson, Henry, 1994. "Long-Run Relations between Private and Public Sector Wages in Sweden," Empirical Economics, Springer, vol. 19(3), pages 343-360.

    More about this item

    Keywords

    time series; unit root; cointegration; error correction; model identification; Smith form;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:ifwedp:201633. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iwkiede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.