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VAR models are a type of multi-equation model that have been widely appliedin econometrics. With the arrival of Big Data, huge amounts of data are beingcollected in numerous fields, making feasible the application of these kind ofstatistical models. Tools exist to tackle this problem, but the large amount ofdata, along with the availability of computational techniques and highperformance systems, advise an in-depth analysis of the computational aspectsof VAR, so large models can be solved efficiently with today's computationalsystems.  This work aims to solve a VAR model by obtaining the coefficients throughheuristic and metaheuristic algorithms, minimizing one parameter criterion, andalso to compare with those coefficients obtained by OLS. Furthermore, weconsider different approaches to reduce the time required to find the modellike using matrix decompositions (QR or LQ), exploiting matrix structure, usinghigh performance linear algebra subroutines (BLAS and LAPACK) or parallelmetaheuristics.

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