- Date: 5 Aug. (Thu.)
- Place: On the Web
- Time: 16:30 – 18:00
- Speaker: Benjamin Poignard (Osaka University)
- Title: Estimation of High Dimensional Vector Autoregression via Sparse Precision Matrix
- Abstract:
We consider the problem of estimating sparse structural vector autoregression (SVAR) processes via penalized precision matrix. This matrix is the output of the underlying directed acyclic graph of the SVAR process, whose zero components correspond to zero SVAR coefficients. 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.
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