ON THE CAUSALITY TEST IN TIME SERIES MODELS WITH HEAVY-TAILED DISTRIBUTION

DOI10.1081/SAC-120003341zbMath1081.62553OpenAlexW2011672541MaRDI QIDQ4416923

Eun-Hee Kim, Sangyeol Lee

Publication date: 5 August 2003

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1081/sac-120003341

zbMATH Keywords

Granger causalityHeavy-tailed distributionNormal mixture distributionStationary time series model

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (1)

Time Series Mixtures of GeneralizedtExperts: ML Estimation and an Application to Stock Return Density Forecasting

Cites Work

• Forecasting in the presence of large shocks
• Causality in temporal systems. Characterizations and a Survey
• Partially adaptive estimation via a normal mixture
• Large-sample tests of homogeneity for time series models
• Testing Causality Between Two Vectors in Multivariate Autoregressive Moving Average Models
• Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model
• Partially adaptive estimation of nonlinear models via a normal mixture
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods

This page was built for publication: ON THE CAUSALITY TEST IN TIME SERIES MODELS WITH HEAVY-TAILED DISTRIBUTION