Work

Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information

Year: 2025

Type: article

Abstract: Vector autoregression (VAR) models are popular in modeling multivariate time series in data sciences and other areas. When the number of time series is large, the number of parameters in the VAR model increases dramatically, posing great challenges for proper model estimation and inference. In this work, we propose a so-called neighborhood vector autoregression (NVAR) model to efficiently analyze large-dimensional multivariate time series. We assume that the time series have underlying neighborhood relationships, e.g., spatial or network, among them based on the inherent setting of the problem. When this neighborhood information is available or can be summarized using a distance matrix, we demonstrate that our proposed NVAR method provides a computationally efficient and theoretically sound estimation of model parameters. The performance of the proposed method is compared with other existing approaches in both simulation studies and a real-data application in environmental science.

Source: Journal of Statistical Planning and Inference

DOI: 10.1016/j.jspi.2025.106277

Authors: Zhihao Hu, Shyam Ranganathan, Yang Shao, Xinwei Deng

Institutions: Virginia Tech, Clemson University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,230

Funder: National Science Foundation