On a Partially Non-Stationary Vector AR Model with Vector GARCH Noises: Estimation and Testing

On a Partially Non-Stationary Vector AR Model with Vector GARCH Noises: Estimation and Testing

Year:    2024

Author:    Chor-yiu Sin, Zichuan Mi, Shiqing Ling

Communications in Mathematical Research , Vol. 40 (2024), Iss. 1 : pp. 64–101

Abstract

 This paper studies a partially nonstationary vector autoregressive (VAR) model with vector GARCH noises. We study the full rank and the reduced rank quasi-maximum likelihood estimators (QMLE) of parameters in the model. It is shown that both QMLE of long-run parameters asymptotically converge to a functional of two correlated vector Brownian motions. Based these, the likelihood ratio (LR) test statistic for cointegration rank is shown to be a functional of the standard Brownian motion and normal vector, asymptotically. As far as we know, our test is new in the literature. The critical values of the LR test are simulated via the Monte Carlo method. The performance of this test in finite samples is examined through Monte Carlo experiments. We apply our approach to an empirical example of three interest rates.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cmr.2023-0005

Communications in Mathematical Research , Vol. 40 (2024), Iss. 1 : pp. 64–101

Published online:    2024-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    38

Keywords:    Vector AR model cointegration full rank estimation vector GARCH process partially nonstationary reduced rank estimation.

Author Details

Chor-yiu Sin

Zichuan Mi

Shiqing Ling