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The impact of integrated measurement errors on modeling long-run macroeconomic time series

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  • James A. Duffy
  • David F. Hendry

Abstract

Data spanning long time periods, such as that over 1860–2012 for the UK, seem likely to have substantial errors of measurement that may even be integrated of order one, but which are probably cointegrated for cognate variables. We analyze and simulate the impacts of such measurement errors on parameter estimates and tests in a bivariate cointegrated system with trends and location shifts which reflect the many major turbulent events that have occurred historically. When trends or shifts therein are large, cointegration analysis is not much affected by such measurement errors, leading to conventional stationary attenuation biases dependent on the measurement error variance, unlike the outcome when there are no offsetting shifts or trends.

Suggested Citation

  • James A. Duffy & David F. Hendry, 2017. "The impact of integrated measurement errors on modeling long-run macroeconomic time series," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 568-587, October.
  • Handle: RePEc:taf:emetrv:v:36:y:2017:i:6-9:p:568-587
    DOI: 10.1080/07474938.2017.1307177
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    References listed on IDEAS

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    1. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 285-318, June.
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    Cited by:

    1. Santiago J. Gahn & Alejandro González, 2018. "On the “utilisation controversy”: a comment," Working Papers PKWP1814, Post Keynesian Economics Society (PKES).

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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