Title: Inference of grouped time-varying network vector autoregression models Authors:  Degui Li - University of York (United Kingdom) [presenting]
Abstract: Statistical inference of time-varying network vector autoregression models for large-scale time series is considered. A latent group structure is imposed on the heterogenous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with normalized distance matrix estimates is combined with a generalized information criterion to consistently estimate the latent group number and membership. A post-grouping local linear smoothing method is proposed to estimate the group-specific time-varying momentum and network effects, substantially improving the convergence rates of the preliminary estimates, which ignore the latent structure. In addition, a post-grouping specification test is conducted to verify the validity of the parametric model assumption for group-specific time-varying coefficient functions. The asymptotic theory is derived for the test statistic constructed via a kernel-weighted quadratic form under the null and alternative hypotheses. Numerical studies are provided to examine the finite-sample performance of the developed model and methodology.