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Open AccessArticle

Conditional Granger Causality and Genetic Algorithms in VAR Model Selection

Department or International Business and Economics, The Bucharest University of Economic Studies, Bucharest 010374, Romania
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Symmetry 2019, 11(8), 1004; https://doi.org/10.3390/sym11081004
Received: 28 May 2019 / Revised: 9 July 2019 / Accepted: 28 July 2019 / Published: 3 August 2019
(This article belongs to the Special Issue Advance in Nonlinear Analysis and Optimization)
Overcoming symmetry in combinatorial evolutionary algorithms is a challenge for existing niching methods. This research presents a genetic algorithm designed for the shrinkage of the coefficient matrix in vector autoregression (VAR) models, constructed on two pillars: conditional Granger causality and Lasso regression. Departing from a recent information theory proof that Granger causality and transfer entropy are equivalent, we propose a heuristic method for the identification of true structural dependencies in multivariate economic time series. Through rigorous testing, both empirically and through simulations, the present paper proves that genetic algorithms initialized with classical solutions are able to easily break the symmetry of random search and progress towards specific modeling. View Full-Text
Keywords: vector autoregression; genetic algorithms; combinatorial symmetry; structural dependence; time series vector autoregression; genetic algorithms; combinatorial symmetry; structural dependence; time series
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Marica, V.G.; Horobet, A. Conditional Granger Causality and Genetic Algorithms in VAR Model Selection. Symmetry 2019, 11, 1004.

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