Next Article in Journal
Applications of Canonical Quantum Gravity to Cosmology
Previous Article in Journal
Numerical Simulation of a Multiscale Cell Motility Model Based on the Kinetic Theory of Active Particles
Previous Article in Special Issue
Limit Analysis of Progressive Asymmetrical Collapse Failure of Tunnels in Inclined Rock Stratum
Article Menu

Export Article

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
*
Author to whom correspondence should be addressed.
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)
  |  
PDF [2417 KB, uploaded 3 August 2019]
  |  

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. View Full-Text
Keywords: vector autoregression; genetic algorithms; combinatorial symmetry; structural dependence; time series vector autoregression; genetic algorithms; combinatorial symmetry; structural dependence; time series
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Marica, V.G.; Horobet, A. Conditional Granger Causality and Genetic Algorithms in VAR Model Selection. Symmetry 2019, 11, 1004.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top