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

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.