here is my code including what i did for the tsboot:
rm(list = ls())
data=get.hist.quote(instrument = security, startDate, endDate, quote =
qte_list, provider = "yahoo" )
func.model<-list(order = c(func.ar$order,0,0),ar=func.ar$ar)
func.res<-func.res - mean()
d = logret
rg1<- function(n, res) sample(res, n, replace=TRUE)
ran.args = List(ts=log(data[,1],model=func.sim))
On Thu, Dec 1, 2016 at 1:50 PM, Bert Gunter <bgunter.4...@gmail.com> wrote:
> Just briefly to follow up David's comment, though this is mainly about
> statistics and therefore off topic here...
> Bootstrapping time series is a subtle issue that requires familiarity
> with the technical details-- and maybe even current research. The
> tsboot() function gives you several options from which you must choose
> *appropriately* -- or maybe choose something else entirely. The Help
> doc gives you a sense of the difficulties:
> Model based resampling is very similar to the parametric bootstrap and
> all simulation must be in one of the user specified functions. This
> avoids the complicated problem of choosing the block length but relies
> on an accurate model choice being made.
> Phase scrambling is described in Section 8.2.4 of Davison and Hinkley
> (1997). The types of statistic for which this method produces
> reasonable results is very limited and the other methods seem to do
> better in most situations. Other types of resampling in the frequency
> domain can be accomplished using the function boot with the argument
> sim = "parametric".
> Moral: If you don't know what you're doing, seek local expertise to
> help -- remote sites offering suggestions from those who aren't
> familiar with the details of your data and analysis goals (maybe you
> don't need to do this at all!) may lead you to irreproducible
> Bert Gunter
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
> On Thu, Dec 1, 2016 at 7:45 AM, Ashwini Patil <ash369s...@gmail.com>
> > Hi,
> > I want to implement a bootstrap method for time series.
> > I am taking the adj close values from yahoo for NFLX and now I need to
> > bootstrap these values using ARIMA model.
> > here is my code so far:
> > rm(list = ls())
> > library(boot)
> > library(tseries)
> > library(TTR)
> > library(quantmod)
> > library(scales)
> > library(forecast)
> > library(zoo)
> > library(TSA)
> > security<-"NFLX"
> > startDate<-"2012-06-01"
> > endDate<-"2016-10-31"
> > qte_list<-c("AdjClose")
> > data=get.hist.quote(instrument = security, startDate, endDate, quote =
> > qte_list, provider = "yahoo" )
> > logret<-diff(log(data[,1]))
> > fit11<-auto.arima(logret, max.order=10)
> > When i use auto.arima, I get an order of (0,0,0) with non-zero mean.
> > this, I tried to use tsboot function but it is not yielding any answers.
> > Any and all help is appreciated.
> > Thank you!
> > [[alternative HTML version deleted]]
> > ______________________________________________
> > Remail@example.com mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> > and provide commented, minimal, self-contained, reproducible code.
[[alternative HTML version deleted]]
Rfirstname.lastname@example.org mailing list -- To UNSUBSCRIBE and more, see
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.