Estimating Time-Varying Networks With a State-Space Model

34 Pages Posted: 8 Feb 2021

See all articles by Shaowen Liu

Shaowen Liu

University of Padova - Department of Statistical Sciences

Massimiliano Caporin

University of Padua - Department of Statistical Sciences

Sandra Paterlini

University of Trento - Department of Economics and Management

Date Written: December 14, 2020

Abstract

We propose the use of state-space models (SSMs) to estimate dynamic spatial relationships from time series data. At each time step, the weight matrix, capturing the latent state, is updated by a spatial autoregressive model. Specifically, we consider two types of SSM: the first one calibrates the spatial model to a multivariate regression, while the second one updates the spatial matrix by leveraging the maximum likelihood (ML) estimation. Different filtering algorithms are proposed to estimate the state. The simulation results show that the first model performs robustly for all cases, while the performance of the second model is sensitive to the state dimension. In a real-world case study, we estimate the time-varying weight matrices with weekly credit default swap (CDS) data for 16 banks, and show that the methods can identify communities which are coherent with the country-driven partitions.

Keywords: state-space mode, dynamic network, spatial dependence, sequential fiters

JEL Classification: C32, C33, C51.

Suggested Citation

Liu, Shaowen and Caporin, Massimiliano and Paterlini, Sandra, Estimating Time-Varying Networks With a State-Space Model (December 14, 2020). Available at SSRN: https://ssrn.com/abstract=3748283 or http://dx.doi.org/10.2139/ssrn.3748283

Shaowen Liu

University of Padova - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

Massimiliano Caporin (Contact Author)

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

Sandra Paterlini

University of Trento - Department of Economics and Management ( email )

Via Inama 5
Trento, I-38100
Italy

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