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Cointegration analysis under measurement errors

In: Measurement Error: Consequences, Applications and Solutions

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  • Uwe Hassler
  • Vladimir Kuzin

Abstract

We study the effect of errors-in-variables [EV] on cointegration tests and cointegrating regressions. It turns out that the rate of convergence of static ordinary least squares [OLS] estimators is not affected by EV, whereas the limiting distribution does change. However, procedures accounting for short-run dynamics correct for EV at the same time and hence are robust to measurement errors. This is established asymptotically, and the relevance of our findings for finite samples is confirmed through computer experiments. Although our analysis is restricted to selected procedures, we indicate how our results will extend to related statistical techniques.

Suggested Citation

  • Uwe Hassler & Vladimir Kuzin, 2009. "Cointegration analysis under measurement errors," Advances in Econometrics, in: Measurement Error: Consequences, Applications and Solutions, pages 131-150, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(2009)0000024009
    DOI: 10.1108/S0731-9053(2009)0000024009
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    Cited by:

    1. Brunt, Liam & Cannon, Edmund, 2015. "Variations in the price and quality of English grain, 1750–1914: Quantitative evidence and empirical implications," Explorations in Economic History, Elsevier, vol. 58(C), pages 74-92.
    2. Gianfreda, Angelica & Maranzano, Paolo & Parisio, Lucia & Pelagatti, Matteo, 2023. "Testing for integration and cointegration when time series are observed with noise," Economic Modelling, Elsevier, vol. 125(C).

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