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

Bo Zhang (), Jiti Gao and Guangming Pan ()

No 10/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: asymptotic normality; largest eigenvalue; linear process; near unit root test. (search for similar items in EconPapers)
JEL-codes: C21 C32 (search for similar items in EconPapers)
Pages: 36
Date: 2019
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.monash.edu/business/ebs/research/publications/ebs/wp10-2019.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2019-10

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().