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Unidentified Components in Reduced Rank Regression Estimation of ECM's

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Abstract

Reduced rank regression procedures in error correction models (ECM's) permit consistent estimation of the cointegration space but do not provide consistent estimates of individual structural relations when the dimension of the cointegration space is greater than one. Indeed, individual structural cointegrating equations are unidentified without additional a priori restrictions, just as in the conventional simultaneous equations framework. The effect of this lack of identification is explored by considering the distributions and limit distributions of reduced rank regression estimates of unidentified components of the cointegrating matrix in a typical VAR formulation of the ECM. Some recommendations are made for empirical practice.

Suggested Citation

  • Peter C.B. Phillips, 1991. "Unidentified Components in Reduced Rank Regression Estimation of ECM's," Cowles Foundation Discussion Papers 1003, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1003
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    File URL: http://cowles.yale.edu/sites/default/files/files/pub/d10/d1003.pdf
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    References listed on IDEAS

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    1. Park, Joon Y, 1992. "Canonical Cointegrating Regressions," Econometrica, Econometric Society, vol. 60(1), pages 119-143, January.
    2. Toda, Hiro Y & Phillips, Peter C B, 1993. "Vector Autoregressions and Causality," Econometrica, Econometric Society, vol. 61(6), pages 1367-1393, November.
    3. Phillips, P C B, 1991. "Optimal Inference in Cointegrated Systems," Econometrica, Econometric Society, vol. 59(2), pages 283-306, March.
    4. Phillips, P. C. B., 1989. "Spherical matrix distributions and cauchy quotients," Statistics & Probability Letters, Elsevier, vol. 8(1), pages 51-53, May.
    5. Phillips, P C B, 1986. "The Distribution of FIML in the Leading Case," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 27(1), pages 239-243, February.
    6. 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.
    7. Park, J.Y. & Ogaki, M., 1991. "Inference in Cointegrated Models Using VAR Prewhitening to Estimate Shortrun Dynamics," RCER Working Papers 281, University of Rochester - Center for Economic Research (RCER).
    8. Peter C.B. Phillips, 1991. "The Tail Behavior of Maximum Likelihood Estimates of Cointegrating Coefficients in Error Correction Models," Cowles Foundation Discussion Papers 999, Cowles Foundation for Research in Economics, Yale University.
    9. Hiro Y. Toda & Peter C.B. Phillips, 1991. "Vector Autoregression and Causality: A Theoretical Overview and Simulation Study," Cowles Foundation Discussion Papers 1001, Cowles Foundation for Research in Economics, Yale University.
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    Cited by:

    1. Phillips, Peter C B, 1995. "Fully Modified Least Squares and Vector Autoregression," Econometrica, Econometric Society, vol. 63(5), pages 1023-1078, September.
    2. Giannini, Carlo & Lanzarotti, Antonio & Seghelini, Mario, 1995. "A traditional interpretation of macroeconomic fluctuations: The case of Italy," European Journal of Political Economy, Elsevier, vol. 11(1), pages 131-155, March.

    More about this item

    Keywords

    Cointegration; error correction models; identification problem;

    JEL classification:

    • 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

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