Control variate method for stationary processes

Tomoyuki Amano, Masanobu Taniguchi

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    The sample mean is one of the most natural estimators of the population mean based on independent identically distributed sample. However, if some control variate is available, it is known that the control variate method reduces the variance of the sample mean. The control variate method often assumes that the variable of interest and the control variable are i.i.d. Here we assume that these variables are stationary processes with spectral density matrices, i.e. dependent. Then we propose an estimator of the mean of the stationary process of interest by using control variate method based on nonparametric spectral estimator. It is shown that this estimator improves the sample mean in the sense of mean square error. Also this analysis is extended to the case when the mean dynamics is of the form of regression. Then we propose a control variate estimator for the regression coefficients which improves the least squares estimator (LSE). Numerical studies will be given to see how our estimator improves the LSE.

    Original languageEnglish
    Pages (from-to)20-29
    Number of pages10
    JournalJournal of Econometrics
    Volume165
    Issue number1
    DOIs
    Publication statusPublished - 2011 Nov 3

    Fingerprint

    Stationary Process
    Estimator
    Sample mean
    Least Squares Estimator
    Spectral Density Matrix
    Spectral density
    Regression Coefficient
    Mean square error
    Identically distributed
    Stationary process
    Control variate
    Numerical Study
    Regression
    Dependent

    Keywords

    • Control variate method
    • Nonparametric spectral estimator
    • Spectral density matrix
    • Stationary processes

    ASJC Scopus subject areas

    • Economics and Econometrics
    • Applied Mathematics
    • History and Philosophy of Science

    Cite this

    Control variate method for stationary processes. / Amano, Tomoyuki; Taniguchi, Masanobu.

    In: Journal of Econometrics, Vol. 165, No. 1, 03.11.2011, p. 20-29.

    Research output: Contribution to journalArticle

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