Hi everybody.
I was wondering if there's any package available that can handle Mixed Data
Sampling Regression Models.
Thanks a lot for your help.
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Hi everybody.
I use to work with monthly data, however this is my first time working with
weekly data.
I have 4 data bases with monthly data and 1 with weekly data, so I want to
convert my weekly to monthly data in order to have the same periodicity in
all of them.
I've read that package xts can
Hello buddies.
I'm trying to use the "granger.test" function wich is included in MSBVAR
package, but I'm also using the package "var" in order to develop vector
autorregresion analysis.
Everything is well behaved...but when I'm trying to use the "irf" function
when I have both libraries open ther
Hello friends.
I'm wondering what happened to package uroot. I worked quite well with
older versions of R, however with 2.12.1 version it simply doesn't work.
The worst thing is that I look for it in the contributed packages and it
simply doesn't appear.
I want to develop ADF test with seasonal
Hi, I'm using package "vars" and I'm trying to extract the algorithm that
function "predict" contained in that package in order to understand how does
it work.
When I type function "VAR" then all its algorithm appears in R, however if I
try to do the same with "predict" nothing happens...Is there
Hello dudes.
I'm developing VAR analysis based on suggestions made by Horváth in its
paper Canonical Correlation Analysis and Wiener-Granger Causality Tests.
That's the reason I'm looking for if there's any R package to develop Wiener
- Granger Causality Test.
Thanks a lot for your unvaluable he
Hi everybody.
I'm using BFAST program and I have some data to be analized (named
"META.csv" for example). When I call BFAST in the following way:
meta <- read.csv("C:/META.csv")
meta.ts<-ts(meta, frequency=12, start = c(2005,9))
dis<-frequency(meta.ts) / length(meta.ts)
meta.sc<-bfast(meta.ts,
Hi everyone.
I'm having some troubles with STL function to decompose some data.
My issue is that I have monthly data from September 2005 up to August 2010
i. e. 60 observations.
I define it in the following way:
*u<-read.csv("C:/CELEBREX.csv",header = TRUE)
u.ts<-ts(u, start=c(2005,9), frequenc
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