Obtaining the coefficients of a Vector Autoregression Model through minimization of parameter criteria

TL;DR

This paper explores efficient computational methods for estimating VAR model coefficients using heuristic and metaheuristic algorithms, comparing them with traditional OLS, and leveraging advanced matrix techniques and parallel computing.

Contribution

It introduces novel heuristic and metaheuristic approaches for VAR coefficient estimation and analyzes their computational efficiency compared to OLS.

Findings

Metaheuristic algorithms can effectively estimate VAR coefficients.

Using matrix decompositions reduces computation time.

Parallel metaheuristics improve efficiency on large datasets.

Abstract

VAR models are a type of multi-equation model that have been widely applied in econometrics. With the arrival of Big Data, huge amounts of data are being collected in numerous fields, making feasible the application of these kind of statistical models. Tools exist to tackle this problem, but the large amount of data, along with the availability of computational techniques and high performance systems, advise an in-depth analysis of the computational aspects of VAR, so large models can be solved efficiently with today's computational systems. This work aims to solve a VAR model by obtaining the coefficients through heuristic and metaheuristic algorithms, minimizing one parameter criterion, and also to compare with those coefficients obtained by OLS. Furthermore, we consider different approaches to reduce the time required to find the model like using matrix decompositions (QR or LQ), exploiting matrix structure, using high performance linear algebra subroutines (BLAS and LAPACK) or parallel metaheuristics.