A non-parametric approach to non-linear causality testing

Bell, David; Kay, Jim; Malley, Jim

doi:10.1016/0165-1765(95)00791-1

Cited by 48 publications

(37 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To date, parametric modeling remains the basis for Granger causality inference in the frequency domain. While nonparametric Granger causality tests have appeared in the past they are all formulated in the time domain (Bell et al, 1996;Diks and Panchenko, 2006;Hiemstra and Jones, 1994). As the parametric spectral approach requires the autoregressive models of data, concerns have been raised regarding the strong underlying assumptions and its suitability for data with complex power spectral content (Mitra and Pesaran, 1999;see Figure 1 in the Supplementary Material).…”

Section: Discussionmentioning

confidence: 99%

Analyzing information flow in brain networks with nonparametric Granger causality

2008

View full text Add to dashboard Cite

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

show abstract

Section: Discussionmentioning

confidence: 99%

Analyzing information flow in brain networks with nonparametric Granger causality

2008

View full text Add to dashboard Cite

show abstract

“…Later on, Hiemstra and Jones [35] provided an improved version of Baek and Brock [34]. However, despite the test of Hiemstra and Jones [35] is (probably) the most used nonlinear causality test in economics and finance (there are others nonlinear causality tests developed to analyze this kind of data, but these are not commonly used, e.g., Bell et al [36]; Su and White [37]; among others). However, it tends to over-reject the null hypothesis if it is true (Diks and Panchenko [16,38]).…”

Section: Nonlinear Causality Testsmentioning

confidence: 99%

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Polanco-Martínez

Abadie

2016

Energies

View full text Add to dashboard Cite

Abstract:The West Texas Intermediate (WTI) spot price shows high volatility and in 2014 and 2015 when quoted prices declined sharply, long-term prices in future markets were less volatile. These prices are different and diverge depending on how they process fundamental and transitory factors. US tight oil production has been a major innovation with significant macroeconomic effects. In this paper we use WTI spot prices and long-term futures prices, the latter calculated as the expected value with a stochastic model calibrated with the futures quotes of each sample day. These long-term prices are the long-term equilibrium value under risk neutral measurement. In order to analyze potential time-scale relationships between spots and future, we perform a wavelet cross-correlation analysis using a novel wavelet graphical tool recently proposed. To check the direction of the causality, we apply non-linear causality tests to raw data and log returns as well as to the wavelet transform of the spot and futures prices. Our results show that in the spot and futures markets for the period 24 February 2006-2 April 2016 there is a bi-directional causality effect for most time scales (from intra-week to biannual). This suggests that spot and futures prices react simultaneously to new information.

show abstract

“…The class of models that offered a good trade-off between computational complexity and descriptive properties were the use of locally weighted polynomial non-parametric regression Gijbels, 1995, 1996). Bell et al (1996Bell et al ( , 1998 have devised Granger causality measures for a specific class of additive local polynomial models. In a series of recent papers Valdes and co-workers (reviewed in Valdes et al (1999)) have applied Local Linear polynomial regression to the analysis of neural signals.…”

Section: Third Generation Influence Measures: Non-linear Granger Causmentioning

confidence: 99%

Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex

Freiwald

Valdés

Bosch

et al. 1999

Journal of Neuroscience Methods

116

View full text Add to dashboard Cite

A non-parametric approach to non-linear causality testing

Cited by 48 publications

References 4 publications

Analyzing information flow in brain networks with nonparametric Granger causality

Analyzing information flow in brain networks with nonparametric Granger causality

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex

Contact Info

Product

Resources

About