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Measurement Error in a First-order Autoregression

Author

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  • Philip Hans Franses

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands)

Abstract

The Ordinary Least Squares (OLS) estimator for the slope parameter in a first-order autoregressive model is biased when the variable is measured with error. Such an error may occur with revisions of macroeconomic data. This paper illustrates and proposes a simple procedure to alleviate the bias, and is based on Total Least Squares (TLS). TLS is, in general, consistent, and also works well in small samples. Simulation experiments and an empirical example show the usefulness of this method.

Suggested Citation

  • Philip Hans Franses, 2020. "Measurement Error in a First-order Autoregression," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 1-14, June.
  • Handle: RePEc:aag:wpaper:v:24:y:2020:i:2:p:1-14
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    References listed on IDEAS

    as
    1. Heij, Christiaan & de Boer, Paul & Franses, Philip Hans & Kloek, Teun & van Dijk, Herman K., 2004. "Econometric Methods with Applications in Business and Economics," OUP Catalogue, Oxford University Press, number 9780199268016.
    2. Staudenmayer, John & Buonaccorsi, John P., 2005. "Measurement Error in Linear Autoregressive Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 841-852, September.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Errors-in-variables; OLS; First-order autoregression; Total Least Squares;

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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