  In data science, vector autoregression (VAR) models are popular in modeling
multivariate time series in the environmental sciences and other applications.
However, these models are computationally complex with the number of parameters
scaling quadratically with the number of time series.
  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 application of stream
nitrogen study.

Methodology;Applications