delta {tsDyn} | R Documentation |
delta statistic of conditional indipendence 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 indipendence 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 indipendence 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)))