R: Surrogate Time Series Through A Modeling (Smoothed Sieve...
surrogate.ARs
R Documentation
Surrogate Time Series Through A Modeling (Smoothed Sieve Bootstrap)
Description
Starting from a time series x given as input, the function generates surrogate series by means of the smoothed sieve bootstrap.
The surrogates can be used for testing for non linearity in time series.
a univariate numeric time series object or a numeric vector.
order.max
maximum order of the AR model to fit.
fit.method
character string giving the method used to fit the AR model.
Must be one of the strings in the default argument (the first few characters are sufficient). Defaults to "yule-walker".
nsurr
number of surrogates.
Details
N is the length of the series x. The best AR model is chosen by means of the AIC criterion. Surrogate series are obtained by driving the fitted model with
the smoothed resampled residuals. Smoothing is performed through
Kernel density estimation with a Gaussian Kernel by using the
dafaults of density.
Value
A list with the following elements:
surr
a matrix with N rows and nsurr columns, in each column is stored a surrogate.
call
contains the call to the routine.
Author(s)
Simone Giannerini<simone.giannerini@unibo.it>
References
Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing
for nonlinear serial dependence in time series, Biometrika, forthcoming
http://doi.org/10.1093/biomet/asv007.
Bickel, P., Buhlmann, P., (1999). A new mixing notion and
functional central limit theorems for a sieve bootstrap in time
series. Bernoulli5, 413–446.
See Also
See also surrogate.AR, Trho.test.AR, surrogate.SA, Trho.test.SA.
Examples
set.seed(1345)
# Generates a AR(1) series
x <- arima.sim(n=120, model = list(ar=0.8));
x.surr <- surrogate.ARs(x, order.max=10, nsurr=3);
plot.ts(x.surr$surr,col=4);
## Check that the surrogates have the same ACF of x
corig <- acf(x,10,plot=FALSE)$acf[,,1];
csurr <- acf(x.surr$surr[,1],10,plot=FALSE)$acf[,,1];
round(cbind(corig,csurr,"abs(difference)"=abs(corig-csurr)),3)