similar to: multiple time series

Dear R-users, tsboot fails when I try to perform a block bootstrap on seaKen (package wq): these commands: require(wq) require(datasets) boot.block.sen <- function(data){seaKen(data)[[1]]} tsboot(sunspot.month, boot.block.sen, R=1999, l=12, sim="fixed") return: Error in seaKen(data) : x must be a 'ts' Any suggestion on how might I change seaKen in order to use it with

I'm trying to bootstrap some regression coefficients so that I can estimate confidence intervals, but boot is not producing results. Can anybody suggest what I'm doing wrong here? > SpecPress <- ts(rnorm(501)) > Returns <- ts(rnorm(501)) > BootData <- data.frame(cbind(SpecPress, Returns)) > boot.specpress <- function(data, indices, maxit=20){ + data <-

Hello everybody, when I create an object of class boot with the function tsboot() from the package boot and try to compute several types of confidence intervals with boot.ci("object of class boot created with tsboot") I obtain the warning message, that "BCa-intervals are not defined for time-series bootstraps". Does that hold in general? Or is it just not defined in

Dear All, I am working with skewed-t copula in my research recently, so I needed to write an mle procedure instead of using a standard fit one; I stick to the sn package. On subsamples of the entire population that I deal with, everything is fine. However, on the total sample (difference in cross-sectional dimension: 30 vs 240) things go wrong - the objective function diverges to infinity. I

Does anyone know how p-values can be generated if tsboot (stationary bootstrap) for time series is performed? That would be of great help. Thanks a lot for your comments. Markus [[alternative HTML version deleted]]

Dear Lillian, I tried to estimate parameters for time series regression using time series bootstrapping as described on page 434 in Davison & Hinkley (1997) - bootstrap methods and their application. This approach is based on an AR process (ARIMA model) with a regression term (compare also with page 414 in Venable & Ripley (2002) - modern applied statistics with S) I rewrote the code

Fritz, I have slightly adapted (didn't work before) "tsboot" from the "boot" library to the current time series conventions of R. The following patch will do that. I suggest to apply this patch to the file "boot/R/bootfuns.q" of the "boot" library at CRAN. best Adrian --- bootfuns.orig.q Thu Dec 9 10:07:23 1999 +++ bootfuns.q Thu Dec 9 10:06:51 1999

Fritz, I have slightly adapted (didn't work before) "tsboot" from the "boot" library to the current time series conventions of R. The following patch will do that. I suggest to apply this patch to the file "boot/R/bootfuns.q" of the "boot" library at CRAN. best Adrian --- bootfuns.orig.q Thu Dec 9 10:07:23 1999 +++ bootfuns.q Thu Dec 9 10:06:51 1999

Dear R experts,   I'm running the following stationary bootstrap programming to find the parameters estimate of a linear model:     X<-runif(10,0,10) Y<-2+3*X a<-data.frame(X,Y) coef<-function(fit){   fit <- lm(Y~X,data=a)    return(coef(fit)) }  result<- tsboot(a,statistic=coef(fit),R = 10,n.sim = NROW(a),sim = "geom",orig.t = TRUE)   Unfortunately, I got this

Dear All, I am using the pmst function from the sn package (version 0.4-0). After inserting the example from the help page, I get non-trivial answers, so everything is fine. However, when I try to extend it to higher dimension: xi <- alpha <- x <- rep(0,27) Omega <- diag(0,27) p1 <- pmst(x, xi, Omega, alpha, df = 5) I get the following result: >p1 [1] 0 attr(,"error")

Hi, I have a time series (say "x") of 13 years showing an evident increase. I want to exclude two observations (the fourth and 10th), so I do: > trend.test(x[-c(4,10)]) where: > x[-c(4,10)] [1] 7 37 79 72 197 385 636 705 700 1500 1900 and I get: Spearman's rank correlation rho data: x[-c(4, 10)] and time(x[-c(4, 10)]) S = 4, p-value < 2.2e-16

Folks, I have a time-series of 875 readings of the weekly returns of a stock market index (India's Nifty). I am interested in the AR(1) coefficient. When I do arima(r, order=c(1,0,0)) I get a statistically significant AR1 coefficient. If we apply the ordinary bootstrap to this problem, this involves sampling with replacement, which destroys the time-series structure. Hence, if we do

Hello, I am have fitted GLS models to time series data. Now I wish to bootstrap this data to produce confidence intervals for the model. However, because this is time series data, normal bootstrapping is not applicable. Secondly, 'tsboot' appears to only be useful for ar models - and does not seem to be applicable to GLS models. I have written code in R to randomly sample blocks of

Hi, I have a time series (say "x") of 13 years showing an evident increase. I want to exclude two observations (the fourth and 10th), so I do: > trend.test(x[-c(4,10)]) where: > x[-c(4,10)] [1] 7 37 79 72 197 385 636 705 700 1500 1900 and I get: Spearman's rank correlation rho data: x[-c(4, 10)] and time(x[-c(4, 10)]) S = 4, p-value < 2.2e-16

Dear R users, Version 0.30-6 of the np package has been uploaded to CRAN. See http://cran.r-project.org/package=np Note that the cubature package is now required in addition to the boot package. The recent updates in 0.30-4 through 0.30-6 provides additional functionality in the form of five new functions that incorporate frequently requested nonparametric entropy-based testing methods to the

Dear R users, Version 0.30-6 of the np package has been uploaded to CRAN. See http://cran.r-project.org/package=np Note that the cubature package is now required in addition to the boot package. The recent updates in 0.30-4 through 0.30-6 provides additional functionality in the form of five new functions that incorporate frequently requested nonparametric entropy-based testing methods to the

Hola Andrés.Necesariamente debe ser con boostrap??Te recuerdo que los modelos ARIMA sirven para modelar la media, pero si el componente volátil (varianza) es significativo , debes usar los modelos GARCH, existe una librería para esto (fGarch)R tiene algunas herramientas para pronósticos. Puedes usar la libraría forecast, que tiene algunos modelos de ajuste, también puedes intentar realizando

http://bugzilla.mindrot.org/show_bug.cgi?id=905 Summary: scp exit status is -1 instead of 0 Product: Portable OpenSSH Version: 3.8.1p1 Platform: ix86 OS/Version: Linux Status: NEW Severity: normal Priority: P2 Component: scp AssignedTo: openssh-bugs at mindrot.org ReportedBy: maik at

Dear R Users, If a stationary bootstrap (Politis & Romano 1994) for time series is performed (e.g. performance difference between trading strategy and benchmark), how can the following data be generated respectively adjusted? 1. p-values 2. smoothing parameter 3. significance levels 4. block lengths Please let me know. Thanks a lot. Best regards, Markus

Hello, The company is in the final stages of implementing in a production environment it economic capital model in order to comply with Solvency II using a partial internal model. The core modelling infrastructure consist of component written in R for pre- and post- processing, and a Java component for Monte Carlo simulation and applying risk mitigation (reinsurance). We are looking for the right