View source: R/Srho.ts_files.R
surrogate.AR | R Documentation |
Starting from a time series x
given as input, the function generates surrogate series by means of the sieve bootstrap.
The surrogates can be used for testing for non linearity in time series.
surrogate.AR(x, order.max = NULL, fit.method = c("yule-walker",
"burg", "ols", "mle", "yw"), nsurr)
x |
a univariate numeric time series object or a numeric vector. |
order.max |
maximum order of the AR model to fit. Passed to |
.
fit.method |
character string giving the method used to fit the AR model. It is passed to |
nsurr |
number of surrogates. |
Let N
be the length of the series x
. The best AR model is chosen by means of the AIC criterion. The residuals of the model are
resampled with replacement. Surrogate series are obtained by driving the fitted model with the resampled residuals.
A list with the following elements:
surr |
a matrix with |
call |
contains the call to the routine. |
Simone Giannerini<simone.giannerini@unibo.it>
Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/asv007")}.
Buhlmann, P., (1997). Sieve bootstrap for time series. Bernoulli, 3, 123–148.
See also surrogate.AR
, Trho.test.AR
, surrogate.SA
, Trho.test.SA
.
set.seed(1345)
# Generates a AR(1) series
x <- arima.sim(n=120, model = list(ar=0.8));
x.surr <- surrogate.AR(x, 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)
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