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Robust Transformations in Univariate and Multivariate Time Series

Author

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  • Marco Riani

Abstract

It is well known that transformation of the response may improve the homogeneity and the approximate normality of the errors. Unfortunately, the estimated transformation and related test statistic may be sensitive to the presence of one, or several, atypical observations. In addition, it is important to remark that outliers in one transformed scale may not be atypical in another scale. Therefore, it is important to choose a transformation which does not depend on the presence of particular observations. In this article we suggest an efficient procedure based on a robust score test statistic which quantifies the effect of each observation on the choice of the transformation.

Suggested Citation

  • Marco Riani, 2009. "Robust Transformations in Univariate and Multivariate Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 262-278.
    • Handle: RePEc:taf:emetrv:v:28:y:2009:i:1-3:p:262-278
    DOI: 10.1080/07474930802388074
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    Cited by:

    1. Atkinson, Anthony C. & Riani, Marco & Corbellini, Aldo, 2021. "The box-cox transformation: review and extensions," LSE Research Online Documents on Economics 103537, London School of Economics and Political Science, LSE Library.
    2. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.

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