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On the errors-in-variables problem for time series

Author

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  • Robinson, P. M.

Abstract

The usual assumption in the classical errors-in-variables problem of independent measurement errors cannot necessarily be maintained when the data are time series; errors may be strongly serially correlated, possibly containing seasonal effects and trends. When it is possible to identify frequency bands over which the signal-to-noise ratio is large, an approximate solution to the errors-in-variables problem is to omit the remaining frequencies from a time series regression. We draw attention to the danger of "leakage" from the omitted frequencies, and show that the consequent bias can be reduced by means of tapering.

Suggested Citation

  • Robinson, P. M., 1986. "On the errors-in-variables problem for time series," Journal of Multivariate Analysis, Elsevier, vol. 19(2), pages 240-250, August.
    • Handle: RePEc:eee:jmvana:v:19:y:1986:i:2:p:240-250
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    Citations

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    Cited by:

    1. Robinson, P.M. & Iacone, F., 2005. "Cointegration in fractional systems with deterministic trends," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 263-298.
    2. Kalnina, Ilze & Linton, Oliver, 2008. "Estimating quadratic variation consistently in the presence of endogenous and diurnal measurement error," Journal of Econometrics, Elsevier, vol. 147(1), pages 47-59, November.
    3. Chanda, Kamal C., 1999. "Bahadur-Kiefer representations for GM-estimators in linear Markov models with errors in variables," Statistics & Probability Letters, Elsevier, vol. 42(4), pages 401-408, May.
    4. Peter M Robinson & Carlos Velasco, 2000. "Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series - (Now published in Journal of the American Statistical Association, 95, (2000), pp.1229-1243.)," STICERD - Econometrics Paper Series 391, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. repec:cte:wsrepe:4554 is not listed on IDEAS
    6. repec:cte:wsrepe:4553 is not listed on IDEAS
    7. Velasco, Carlos, 1999. "Non-stationary log-periodogram regression," Journal of Econometrics, Elsevier, vol. 91(2), pages 325-371, August.
    8. Brillinger, David R., 1996. "Remarks Concerning Graphical Models for Time Series and Point Processes," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 16(1), November.
    9. Robinson, Peter M. & Velasco, Carlos, 2000. "Whittle pseudo-maximum likelihood estimation for nonstationary time series," LSE Research Online Documents on Economics 2273, London School of Economics and Political Science, LSE Library.
    10. Kamal C. Chanda, 1995. "Large Sample Analysis Of Autoregressive Moving‐Average Models With Errors In Variables," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(1), pages 1-15, January.
    11. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.

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