Yule–Walker Equations Using a Gini Covariance Matrix for the High-Dimensional Heavy-Tailed PVAR Model

Jin Zou and Dong Han
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Jin Zou: School of Mathematics Science, Shanghai Jiao Tong University, Shanghai 200240, China
Dong Han: School of Mathematics Science, Shanghai Jiao Tong University, Shanghai 200240, China

Mathematics, 2021, vol. 9, issue 6, 1-15

Abstract: Gini covariance plays a vital role in analyzing the relationship between random variables with heavy-tailed distributions. In this papaer, with the existence of a finite second moment, we establish the Gini–Yule–Walker equation to estimate the transition matrix of high-dimensional periodic vector autoregressive (PVAR) processes, the asymptotic results of estimators have been established. We apply this method to study the Granger causality of the heavy-tailed PVAR process, and the results show that the robust transfer matrix estimation induces sign consistency in the value of Granger causality. Effectiveness of the proposed method is verified by both synthetic and real data.

Keywords: high dimensional time series; transition matrix estimation; Gini covariance matrix; U-statistics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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