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Orthogonal Samples for Estimators in Time Series

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  • Suhasini Subba Rao

Abstract

Inference for statistics of a stationary time series often involves nuisance parameters and sampling distributions that are difficult to estimate. In this paper, we propose the method of orthogonal samples, which can be used to address some of these issues. For a broad class of statistics, an orthogonal sample is constructed through a slight modification of the original statistic such that it shares similar distributional properties as the centralized statistic of interest. We use the orthogonal sample to estimate nuisance parameters of the weighted average periodogram estimators and L2†type spectral statistics. Further, the orthogonal sample is utilized to estimate the finite sampling distribution of various test statistics under the null hypothesis. The proposed method is simple and computationally fast to implement. The viability of the method is illustrated with various simulations.

Suggested Citation

  • Suhasini Subba Rao, 2018. "Orthogonal Samples for Estimators in Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(3), pages 313-337, May.
    • Handle: RePEc:bla:jtsera:v:39:y:2018:i:3:p:313-337
    DOI: 10.1111/jtsa.12269
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    Cited by:

    1. von Sachs, Rainer, 2019. "Spectral Analysis of Multivariate Time Series," LIDAM Discussion Papers ISBA 2019008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Sourav Das & Suhasini Subba Rao & Junho Yang, 2021. "Spectral methods for small sample time series: A complete periodogram approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 597-621, September.

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