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Inference of Grouped Time-Varying Network Vector Autoregression Models

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posted on 2025-04-09, 00:00 authored by Degui Li, Bin Peng, Songqiao Tang, Weibiao Wu

This paper considers statistical inference of time-varying network vector autoregression models for large-scale time series. A latent group structure is imposed on the heterogeneous 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, and the asymptotic theory is derived for the test statistic constructed via a kernel weighted quadratic form under the null and alternative hypotheses. Numerical studies including Monte-Carlo simulation and an empirical application to the global trade flow data are presented to examine the finite-sample performance of the developed model and methodology.

History

Classification-JEL

--

Creation date

2023-04-01

Working Paper Series Number

5/23

Length

55 pp

File-Format

application/pdf

Handle

RePEc:msh:ebswps:2023-5

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