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Estimating Restricted Cointegrating Vectors

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  • Elliott, Graham

Abstract

This paper 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 which are linearly or nonlinearly related to the unrestricted cointegrating vector coefficients. The limiting distribution of the estimates as well as 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, 1999. "Estimating Restricted Cointegrating Vectors," University of California at San Diego, Economics Working Paper Series qt5sr55716, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt5sr55716
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    References listed on IDEAS

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    1. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
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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. Atle Oglend & Morten E. Lindback & Petter Osmundsen, 2016. "Shale Gas Boom Affecting the Relationship Between LPG and Oil Prices," The Energy Journal, , vol. 37(1), pages 211-232, January.
    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. 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.
    5. 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.
    6. 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.

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