A non-parametric approach to non-linear causality testing

DOI10.1016/0165-1765(95)00791-1zbMath0875.90178OpenAlexW2067377518MaRDI QIDQ1351101

O. Diekmann

Publication date: 27 February 1997

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0165-1765(95)00791-1

zbMATH Keywords

Non-linear Granger causalityNon-parametric estimation

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Statistical methods; economic indices and measures (91B82)

Related Items (8)

Quantile transfer entropy: measuring the heterogeneous information transfer of nonlinear time series ⋮ Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models ⋮ Linear and nonlinear causality tests in an LSTAR model: wavelet decomposition in a nonlinear environment ⋮ A model-free characterization of causality ⋮ A new statistic and practical guidelines for nonparametric Granger causality testing ⋮ Testing the Granger Noncausality Hypothesis in Stationary Nonlinear Models of Unknown Functional Form ⋮ Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods ⋮ A NONPARAMETRIC HELLINGER METRIC TEST FOR CONDITIONAL INDEPENDENCE

Cites Work

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• Linear smoothers and additive models
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Economic processes involving feedback

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