llag.test | R Documentation |
Tests the absence of lead-lag effects (time-lagged correlations) by the wild bootstrap procedure proposed in Koike (2017) for each pair of components.
llag.test(x, from = -Inf, to = Inf, division = FALSE, grid, R = 999, parallel = "no", ncpus = getOption("boot.ncpus", 1L), cl = NULL, tol = 1e-06)
x |
an object of |
from |
a numeric vector each of whose component(s) indicates the lower end of a finite grid on which the contrast function is evaluated, if |
to |
a numeric vector each of whose component(s) indicates the upper end of a finite grid on which the contrast function is evaluated, if |
division |
a numeric vector each of whose component(s) indicates the number of the points of a finite grid on which the contrast function is evaluated, if |
grid |
a numeric vector or a list of numeric vectors. See 'Details' of |
R |
a single positive integer indicating the number of bootstrap replicates. |
parallel |
passed to |
ncpus |
passed to |
cl |
passed to |
tol |
tolelance parameter to avoid numerical errors in comparison of time stamps. All time stamps are divided by |
For each pair of components, this function performs the wild bootstrap procedure proposed in Koike (2017) to test whether there is a (possibly) time-lagged correlation. The null hypothesis of the test is that there is no time-lagged correlation and the alternative is its negative. The test regects the null hypothesis if the maximum of the absolute values of cross-covariances is too large. The critical region is constructed by a wild bootstrap procedure with Rademacher variables as the multiplier variables.
p.values |
a matrix whose components indicate the bootstrap p-values for the corresponding pair of components. |
max.cov |
a matrix whose componenets indicate the maxima of the absolute values of cross-covariances for the corresponding pair of components. |
max.corr |
a matrix whose componenets indicate the maxima of the absolute values of cross-correlations for the corresponding pair of components. |
Yuta Koike with YUIMA Project Team
Koike, Y. (2019). Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data, Annals of Statistics, 47, 1663–1687. doi: 10.1214/18-AOS1731.
cce
, hyavar
, mllag
, llag
## Not run: # The following example is taken from mllag ## Set a model diff.coef.matrix <- matrix(c("sqrt(x1)", "3/5*sqrt(x2)", "1/3*sqrt(x3)", "", "4/5*sqrt(x2)","2/3*sqrt(x3)", "","","2/3*sqrt(x3)"), 3, 3) drift <- c("1-x1","2*(10-x2)","3*(4-x3)") cor.mod <- setModel(drift = drift, diffusion = diff.coef.matrix, solve.variable = c("x1", "x2","x3")) set.seed(111) ## We use a function poisson.random.sampling ## to get observation by Poisson sampling. yuima.samp <- setSampling(Terminal = 1, n = 1200) yuima <- setYuima(model = cor.mod, sampling = yuima.samp) yuima <- simulate(yuima,xinit=c(1,7,5)) ## intentionally displace the second time series data2 <- yuima@data@zoo.data[[2]] time2 <- time(data2) theta2 <- 0.05 # the lag of x2 behind x1 stime2 <- time2 + theta2 time(yuima@data@zoo.data[[2]]) <- stime2 data3 <- yuima@data@zoo.data[[3]] time3 <- time(data3) theta3 <- 0.12 # the lag of x3 behind x1 stime3 <- time3 + theta3 time(yuima@data@zoo.data[[3]]) <- stime3 ## sampled data by Poisson rules psample<- poisson.random.sampling(yuima, rate = c(0.2,0.3,0.4), n = 1000) ## We search lead-lag parameters on the interval [-0.1, 0.1] with step size 0.01 G <- seq(-0.1,0.1,by=0.01) ## perform lead-lag test llag.test(psample, grid = G, R = 999) ## Since the lead-lag parameter for the pair(x1, x3) is not contained in G, ## the null hypothesis is not rejected for this pair ## End(Not run)
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