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Local Powers of Least-Squares-Based Test for Panel Fractional Ornstein-Uhlenbeck Process

Author

Listed:
  • Tanaka, Katsuto

    (Gakushuin University)

  • Xiao, Weilin

    (Zhejiang University)

  • Yu, Jun

    (School of Economics, Singapore Management University)

Abstract

Based on the least squares estimator, this paper proposes a novel method to test the sign of the persistence parameter in a panel fractional Ornstein-Uhlenbeck process with a known Hurst parameter H. Depending on H ∈ (1/2, 1), H = 1/2, or H ∈ (0, 1/2), three test statistics are considered. In the null hypothesis the persistence parameter is zero. Based on a panel of continuous record of observations, the null asymptotic distributions are obtained when T is fixed and N is assumed to go to infinity, where T is the time span of the sample and N is the number of cross sections. The power function of the tests is obtained under the local alternative where the persistence parameter is close to zero in the order of 1/(T√N). The local power of the proposed test statistics is computed and compared with that of the maximum-likelihood-based test. The hypothesis testing problem and the local power function are also considered when a panel of discrete-sampled observations is available under a sequential limit.

Suggested Citation

  • Tanaka, Katsuto & Xiao, Weilin & Yu, Jun, 2020. "Local Powers of Least-Squares-Based Test for Panel Fractional Ornstein-Uhlenbeck Process," Economics and Statistics Working Papers 6-2020, Singapore Management University, School of Economics.
  • Handle: RePEc:ris:smuesw:2020_006
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    More about this item

    Keywords

    Panel fractional Ornstein-Uhlenbeck process; Least squares; Asymptotic distribution; Local alternative; Local power;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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