Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model

Yanqin Fan, Fang Han and Hyeonseok Park

Journal of Econometrics, 2023, vol. 237, issue 1

Abstract: This paper develops simple, robust estimation and inference methods for the transition matrix of a high-dimensional semiparametric Gaussian copula vector autoregressive process. Our estimator is based on rank estimators of the large variance and auto-covariance matrices of a transformed latent high-dimensional Gaussian process. We derive rates of convergence of our estimator based on which we develop de-biased inference for Granger causality. Numerical results demonstrate the efficacy of the proposed methods. Although our focus is on the observable process, by the nature of rank estimators, all the methods developed directly apply to the transformed latent process. In technical terms, our analysis relies heavily on newly developed exponential inequalities for (degenerate) U-statistics under α-mixing condition.

Keywords: High-dimensional time series; Sparse transition matrix; α-mixing; Latent Gaussian process; De-biasing inference; Kendall’s tau (search for similar items in EconPapers)
JEL-codes: C32 C51 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:237:y:2023:i:1:s0304407623002294

DOI: 10.1016/j.jeconom.2023.105513

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