Boot.FitFGN {FGN} | R Documentation |
Simulate a realization from a fitted AR model. This is useful in the parametric bootstrap. Generic function for "Boot" method.
## S3 method for class 'FitFGN': Boot(obj, R = 1, ...)
obj |
the output from FitAR |
R |
number of bootstrap replications |
... |
optional arguments |
The method of Davies and Harte (1987) is used if it is applicable, otherwise the Durbin-Levinsion recursion is used.
If R=1, a simulated time series with the same length as the original fitted time series is produced. Otherwise if R>1, a matrix with R columns and number of rows equal to the length of the series containing R replications of the bootstrap.
A.I. McLeod
McLeod, A.I., Yu, Hao, Krougly, Zinovi L. (2007). Algorithms for Linear Time Series Analysis, Journal of Statistical Software.
SimulateFGN
,
DHSimulate
DLSimulate
#Example 1 #Fit a FGN model and determine the bootstrap sd of H #Measure cpu time. With R=250, it takes about 23 sec #on 3.6 GHz Pentium IV. data(NileMin) outNileMin<-FitFGN(NileMin) start<-proc.time()[1] R<-25 Hs<-numeric(R) Z<-Boot(outNileMin, R=R) for (i in 1:R) Hs[i]<-GetFitFGN(Z[,i])$H BootSD<-sd(Hs) #this is the bootstrap sd end<-proc.time()[1] totTim<-end-start