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• W2964389680 abstract "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." @default.
• W2964389680 created "2019-08-13" @default.
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• W2964389680 date "2019-08-03" @default.
• W2964389680 modified "2023-10-16" @default.
• W2964389680 title "Conditional Granger Causality and Genetic Algorithms in VAR Model Selection" @default.
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• W2964389680 doi "https://doi.org/10.3390/sym11081004" @default.
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