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Spurious Regression, Cointegration, and Near Cointegration: A Unifying Approach

Author

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  • Jansson, Michael
  • Haldrup, Niels Prof.

Abstract

This paper introduces a representation of an integrated vector time series in which the coefficient of multiple correlation computed from the long-run covariance matrix of the innovation sequences is a primitive parameter of the model. Based on this representation, we propose a notion of near cointegration, which helps bridging the gap between the polar cases of spurious regression and cointegration. Two applications of the model of near cointegration are provided. As a first application, the properties of conventional cointegration methods under near cointegration are characterized, hereby investigating the robustness of cointegration methods. Secondly, we illustrate how to obtain local power functions of cointegration tests that take cointegration as the null hypothesis.

Suggested Citation

  • Jansson, Michael & Haldrup, Niels Prof., 2000. "Spurious Regression, Cointegration, and Near Cointegration: A Unifying Approach," Department of Economics, Working Paper Series qt5b13w0rp, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    • Handle: RePEc:cdl:econwp:qt5b13w0rp
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    Cited by:

    1. Angela Huang, 2004. "Examining finite-sample problems in the application of cointegration tests for long-run bilateral exchange rates," Reserve Bank of New Zealand Discussion Paper Series DP 2004/08, Reserve Bank of New Zealand.

    More about this item

    Keywords

    cointegration; spurious regression; near cointegration; cointegration tests; local power function; Brownian motion;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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