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A Near Unit Root Test for High-Dimensional Nonstationary Time Series

Author

Listed:
  • Bo Zhang
  • Jiti Gao
  • Guangming Pan

Abstract

This paper considers a p-dimensional time series model of the form x(t)-δ(t)=φ(x(t-1)-δ(t-1))+Σ^(1/2)y(t), 1≤t≤T, where y(t) = (y(t1),...,y(tp)) and Σ^(1/2) is the square root of a symmetric positive definite matrix. Here φ≤1 and T(1-φ) is bounded and the linear processes y(tj) is of the form Σb(k)z(t-k,j) where Σ|bi| < infinity and {z(ij)} are independent and identically distributed (i.i.d.) random variables with E(z(ij))=0, E|z(ij)|^2=1 and E|z(ij)|^4< infinity. We first investigate the asymptotic behavior of the first k largest eigenvalues of the sample covariance matrices of the time series model. We then propose an estimator of φ and use it to test for near unit roots. Simulations and empirical applications are also conducted to demonstrate the performance of the statistic.

Suggested Citation

  • Bo Zhang & Jiti Gao & Guangming Pan, 2019. "A Near Unit Root Test for High-Dimensional Nonstationary Time Series," Monash Econometrics and Business Statistics Working Papers 10/19, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2019-10
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp10-2019.pdf
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    References listed on IDEAS

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    5. Phillips, Peter C B, 1988. "Regression Theory for Near-Integrated Time Series," Econometrica, Econometric Society, vol. 56(5), pages 1021-1043, September.
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    Cited by:

    1. Bo Zhang & Jiti Gao & Guangming Pan & Yanrong Yang, 2019. "Spiked Eigenvalues of High-Dimensional Separable Sample Covariance Matrices," Monash Econometrics and Business Statistics Working Papers 31/19, Monash University, Department of Econometrics and Business Statistics.

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    More about this item

    Keywords

    asymptotic normality; largest eigenvalue; linear process; near unit root test.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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