English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Statistical identification of possibly non-fundamental SVARMA models requiresstructural errors: (i) to be an i.i.d process, (ii) to be mutually independentacross components, and (iii) each of them must be non-Gaussian distributed.Hence, provided the first two requisites, it is crucial to evaluate thenon-Gaussian identification condition. We address this problem by relating thenon-Gaussian dimension of structural errors vector to the rank of a matrixbuilt from the higher-order spectrum of reduced-form errors. This makes ourproposal robust to the roots location of the lag polynomials, and generalizesthe current procedures designed for the restricted case of a causal structuralVAR model. Simulation exercises show that our procedure satisfactorilyestimates the number of non-Gaussian components.

Robust Estimation of the non-Gaussian Dimension in Structural Linear Models