Estimation of High Dimensional Vector Autoregression via Sparse Precision Matrix

Benjamin Poignard () and Manabu Asai
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Benjamin Poignard: Graduate School of Economics, Osaka University

No 21-03, Discussion Papers in Economics and Business from Osaka University, Graduate School of Economics

Abstract: We consider the problem of estimating sparse structural vector autoregression (SVAR) processes via penalized precision matrix. Such matrix is the output of the underlying directed acyclic graph of the SVAR process, whose zero components correspond to zero SVAR coecients. The precision matrix estimators are deduced from the class of Bregman divergences and regularized by the SCAD, MCP and LASSO penalties. Under suitable regularity conditions, we derive error bounds for the regularized precision matrix for each Bregman divergence. Moreover, we establish the support recovery property, including the case when the penalty is non-convex. These theoretical results are supported by empirical studies.

Keywords: sparse structural vector autoregression; statistical consistency; support recovery. (search for similar items in EconPapers)
Pages: 67pages
Date: 2021-04
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Estimation of high-dimensional vector autoregression via sparse precision matrix (2023)
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