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Estimating Restricted Cointegrating Vectors

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  • Elliott, Graham

Abstract

This article suggests the use of simple minimum-distance methods to estimate restricted cointegrating vectors. The method directly employs minimum-distance methods on unrestricted cointegrating matrices estimated in the usual way to estimate restricted parameters that are linearly or nonlinearly related to the unrestricted cointegrating vector coefficients. The limiting distribution of the estimates and the usual test for the restrictions are derived. A Monte Carlo experiment is undertaken to examine the effectiveness of these methods for cointegrating vectors.

Suggested Citation

  • Elliott, Graham, 2000. "Estimating Restricted Cointegrating Vectors," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 91-99, January.
    • Handle: RePEc:bes:jnlbes:v:18:y:2000:i:1:p:91-99
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    Cited by:

    1. 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.
    2. repec:aen:journl:ej36-4-osmundsen is not listed on IDEAS
    3. Hansen, Peter Reinhard, 2003. "Structural changes in the cointegrated vector autoregressive model," Journal of Econometrics, Elsevier, vol. 114(2), pages 261-295, June.
    4. Lindback, Morten & Osmundsen, Petter & Øglend, Atle, 2013. "Shale Gas and the Relationship between U.S. Natural Gas, Liquified Petroleum Gases and Oil Market," UiS Working Papers in Economics and Finance 2013/5, University of Stavanger.
    5. Considine, Timothy J., 2018. "Estimating concave substitution possibilities with non-stationary data using the dynamic linear logit demand model," Economic Modelling, Elsevier, vol. 72(C), pages 22-30.
    6. H. Peter Boswijk & Jurgen A. Doornik, 2004. "Identifying, estimating and testing restricted cointegrated systems: An overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 440-465, November.

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