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A Unified Approach To The Measurement Error Problem In Time Series Models

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  • Tanaka, Katsuto

Abstract

The measurement error problem that we consider in this paper is concerned with the situation where time series data of various kinds—short memory, long memory, and random walk processes—are contaminated by white noise. We suggest a unified approach to testing for the existence of such noise. It is found that the power of our test crucially depends on the underlying process.

Suggested Citation

  • Tanaka, Katsuto, 2002. "A Unified Approach To The Measurement Error Problem In Time Series Models," Econometric Theory, Cambridge University Press, vol. 18(2), pages 278-296, April.
    • Handle: RePEc:cup:etheor:v:18:y:2002:i:02:p:278-296_18
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    Cited by:

    1. Asai, Manabu & McAleer, Michael & Medeiros, Marcelo C., 2012. "Modelling and forecasting noisy realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 217-230, January.
    2. Vadim Marmer, 2008. "Testing the null hypothesis of no regime switching with an application to GDP growth rates," Empirical Economics, Springer, vol. 35(1), pages 101-122, August.
    3. Balakrishna, N. & Kim, Jiwoong & Koul, Hira L., 2020. "Lack-of-fit of a parametric measurement error AR(1) model," Statistics & Probability Letters, Elsevier, vol. 166(C).
    4. Henghsiu Tsai & Heiko Rachinger & Edward M.H. Lin, 2015. "Inference of Seasonal Long-memory Time Series with Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 137-154, March.

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