delta test of conditional independence
delta statistic of conditional independence and associated bootstrap test
delta(x, m, d = 1, eps) delta.test(x, m = 2:3, d = 1, eps = seq(0.5 * sd(x), 2 * sd(x), length = 4), B = 49)
x |
time series |
m |
vector of embedding dimensions |
d |
time delay |
eps |
vector of length scales |
B |
number of bootstrap replications |
delta statistic of conditional independence and associated bootstrap test. For details, see Manzan(2003).
delta returns the computed delta statistic. delta.test
returns the bootstrap based 1-sided p-value.
Results are sensible to the choice of the window
eps. So, try the test for a grid of m and eps values.
Also, be aware of the course of dimensionality: m can't be too high for
relatively small time series. See references for further details.
Antonio, Fabio Di Narzo
Sebastiano Manzan, Essays in Nonlinear Economic Dynamics, Thela Thesis (2003)
BDS marginal independence test: bds.test in
package tseries
Teraesvirta's neural network test for nonlinearity:
terasvirta.test in package tseries
delta test for nonlinearity: delta.lin.test
delta(log10(lynx), m=3, eps=sd(log10(lynx)))
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