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Normalising cointegrating relationships subject to long-run exclusion

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  • Kurita, Takamitsu

Abstract

This paper conducts a comparative simulation study in a recursive manner to illuminate a problem with the normalisation of cointegrating vectors that are subject to long-run exclusion. It indicates that pre-testing for long-run exclusion can play a critical role in revealing interpretable structures from non-stationary time series data.

Suggested Citation

  • Kurita, Takamitsu, 2020. "Normalising cointegrating relationships subject to long-run exclusion," Economics Letters, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:ecolet:v:192:y:2020:i:c:s0165176520301269
    DOI: 10.1016/j.econlet.2020.109161
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    References listed on IDEAS

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    1. Paolo Paruolo, 2006. "The Likelihood Ratio Test for the Rank of a Cointegration Submatrix," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 921-948, December.
    2. Johansen, Søren, 2010. "Some identification problems in the cointegrated vector autoregressive model," Journal of Econometrics, Elsevier, vol. 158(2), pages 262-273, October.
    3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    4. Johansen, Soren & Juselius, Katarina, 1994. "Identification of the long-run and the short-run structure an application to the ISLM model," Journal of Econometrics, Elsevier, vol. 63(1), pages 7-36, July.
    5. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679.
    6. Heino Bohn Nielsen, 2019. "Estimation bias and bias correction in reduced rank autoregressions," Econometric Reviews, Taylor & Francis Journals, vol. 38(3), pages 332-349, March.
    7. Pentti Saikkonen, 1999. "Testing normalization and overidentification of cointegrating vectors in vector autoregressive processes," Econometric Reviews, Taylor & Francis Journals, vol. 18(3), pages 235-257.
    8. Boswijk, H Peter, 1996. "Testing Identifiability of Cointegrating Vectors," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 153-160, April.
    9. Luukkonen, Ritva & Ripatti, Antti & Saikkonen, Pentti, 1999. "Testing for a Valid Normalization of Cointegrating Vectors in Vector Autoregressive Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 195-204, April.
    10. Podivinsky, Jan M., 1992. "Small sample properties of tests of linear restrictions on cointegrating vectors and their weights," Economics Letters, Elsevier, vol. 39(1), pages 13-18, May.
    11. Canepa, Alessandra, 2006. "Small sample corrections for linear restrictions on cointegrating vectors: A Monte Carlo comparison," Economics Letters, Elsevier, vol. 91(3), pages 330-336, June.
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    Cited by:

    1. THW Ziesemer, 2020. "Japan’s Productivity and GDP Growth: The Role of Private, Public and Foreign R&D 1967–2017," Economies, MDPI, vol. 8(4), pages 1-25, September.

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    More about this item

    Keywords

    Cointegrating relationships; Vector autoregressive (VAR) models; Normalisation; Long-run exclusion;
    All these keywords.

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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