MATLAB Source Code for Multivariate Autoregressive (MVAR) Model

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Resource Overview

MATLAB source code for implementing multivariate autoregressive models, providing efficient computation of MVAR coefficients, spectral structure, and connectivity analysis with built-in functions for model estimation and validation.

Detailed Documentation

This MATLAB source code for multivariate autoregressive (MVAR) modeling enables straightforward computation of model coefficients and spectral properties. The implementation includes core functions such as arfit() or varm() for parameter estimation, supporting algorithms like the Yule-Walker equations or least squares methods for optimal coefficient calculation. The computed results facilitate deeper understanding of multivariate data relationships through spectral decomposition and Granger causality analysis, making it valuable for applications such as financial market trend prediction using time-series forecasting techniques, or medical research for analyzing interdependencies between physiological signals. The code structure emphasizes usability with pre-processing utilities for data normalization and post-processing modules for result visualization, allowing researchers to efficiently perform advanced multivariate analysis with minimal configuration.

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