Statistical analysis of a class of factor time series models

Masanobu Taniguchi, Kousuke Maeda, Madan L. Puri

    Research output: Contribution to journalArticle

    Abstract

    For a class of factor time series models, which is called a multivariate time series variance component (MTV) models, we consider the problem of testing whether an observed time series belongs to this class. We propose the test statistic, and derive its symptotic null distribution. Asymptotic optimality of the proposed test is discussed in view of the local asymptotic normality. Also, numerical evaluation of the local power illuminates some interesting features of the test.

    Original languageEnglish
    Pages (from-to)2367-2380
    Number of pages14
    JournalJournal of Statistical Planning and Inference
    Volume136
    Issue number7 SPEC. ISS.
    DOIs
    Publication statusPublished - 2006 Jul 1

    Fingerprint

    Factor Models
    Time Series Models
    Statistical Analysis
    Time series
    Statistical methods
    Local Asymptotic Normality
    Variance Component Model
    Local Power
    Asymptotic Optimality
    Multivariate Time Series
    Null Distribution
    Test Statistic
    Testing
    Evaluation
    Statistics
    Class
    Factors
    Time series models
    Statistical analysis
    Test statistic

    Keywords

    • Factor time series model
    • Local asymptotic normality
    • Local power
    • Periodogram
    • Spectral density

    ASJC Scopus subject areas

    • Statistics, Probability and Uncertainty
    • Applied Mathematics
    • Statistics and Probability

    Cite this

    Statistical analysis of a class of factor time series models. / Taniguchi, Masanobu; Maeda, Kousuke; Puri, Madan L.

    In: Journal of Statistical Planning and Inference, Vol. 136, No. 7 SPEC. ISS., 01.07.2006, p. 2367-2380.

    Research output: Contribution to journalArticle

    Taniguchi, Masanobu ; Maeda, Kousuke ; Puri, Madan L. / Statistical analysis of a class of factor time series models. In: Journal of Statistical Planning and Inference. 2006 ; Vol. 136, No. 7 SPEC. ISS. pp. 2367-2380.
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