Conditional Granger Causality and Genetic Algorithms in VAR Model Selection

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.

Wavelet Multiscale Granger Causality Analysis Based on State Space Models

“…Numerous research studies have sought to minimize estimation bias when applying Granger causality analysis. One of the proposed solutions involves utilizing a combination of lasso regression and genetic algorithm, facilitated by initialization, selection, feature cross over, and mutation, to overcome coefficient overfitting [15]. Similarly, a new unified Granger causality method has been proposed, addressing the inconsistency between model order selection criteria and hypothesis testing procedures [16].…”