Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
International Journal of Approximate Reasoning
Conditional infomax learning: an integrated framework for feature extraction and fusion
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Linear non-Gaussian causal discovery from a composite set of major US macroeconomic factors
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Given a comprehensive set of financial factors, we use linear non-Gaussian SEM to automatically identify the causal relationships buried in the factor set. The causal structure is allowed to have cyclic edges, explicitly accommodating 'mutual causality' which is well acknowledged but rarely modeled in standard economic theory. The method takes advantage of both artificial intelligence and economic related techniques, and identifies one stable model from several distribution-equivalent equilibrium models for each dataset. Empirical studies on 15 financial factors reveal some interesting findings, especially for the risk-return relationship modeling and capital structure determinants discovery.