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Identifying Long-run Behaviour with Non-stationary Data


  • John Hunter


Results for the identification of non-linear models are used to support the traditional form of the order condition by sufficient conditions. The sufficient conditions reveal a two step procedure for firstly checking generic identification and then testing identifiability. This approach can be extended to sub-blocks of the system and it generalizes to non-linear restrictions. The procedure is applied to an empirical model of the exchange rate, which is identified by diagonalising the system.
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Suggested Citation

  • John Hunter, "undated". "Identifying Long-run Behaviour with Non-stationary Data," Economics and Finance Discussion Papers 98-01, Economics and Finance Section, School of Social Sciences, Brunel University.
  • Handle: RePEc:bru:bruedp:98-01

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    References listed on IDEAS

    1. Ericsson, Neil R & Hendry, David F & Mizon, Grayham E, 1998. "Exogeneity, Cointegration, and Economic Policy Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 370-387, October.
    2. Johansen, Soren, 1992. "Testing weak exogeneity and the order of cointegration in UK money demand data," Journal of Policy Modeling, Elsevier, vol. 14(3), pages 313-334, June.
    3. Hunter, J., 1990. "Cointegrating exogeneity," Economics Letters, Elsevier, vol. 34(1), pages 33-35, September.
    4. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    5. Johansen, Soren, 1995. "Identifying restrictions of linear equations with applications to simultaneous equations and cointegration," Journal of Econometrics, Elsevier, vol. 69(1), pages 111-132, September.
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    More about this item

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - 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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