similar to: Different results in the unit root test. Why?

Dear R-list member, an update of package 'urca' has been uploaded to CRAN (Mirror: Austria). In the updated release unit root and cointegration tests encountered in applied econometric analysis are implemented. The package is written in 'pure' R and utilises S4 classes. In particular, the Johansen procedure with likelihood ratio tests for the inclusion of a linear trend,

Hi all, Can anyone clear my doubts about what conclusions to take with the following what puts of some time series tests: > adf.test(melbmax) Augmented Dickey-Fuller Test data: melbmax Dickey-Fuller = -5.4075, Lag order = 15, p-value = 0.01 alternative hypothesis: stationary Warning message: p-value smaller than printed p-value in: adf.test(melbmax)

Dear R users, I have a function (below) which encompasses several tests. However, when I run it, only the output of the last test is displayed. How can I ensure that the function root(var) will run and display the output from all tests, and not just the last one? Thank you, b. root <- function(var) { #---Phillips-Perron PP.test(var, lshort = TRUE) PP.test(var, lshort = FALSE)

Hi R users! I've come across using kpss tests for time series analysis and i have a question that troubles me since i don't have much experience with time series and the mathematical part underlining it. x<-c(253, 252, 275, 275, 272, 254, 272, 252, 249, 300, 244, 258, 255, 285, 301, 278, 279, 304, 275, 276, 313, 292, 302, 322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287,

is that 'x' is level or trend stationary. I did this > s<-rnorm(1000) > kpss.test(s) KPSS Test for Level Stationarity data: s KPSS Level = 0.0429, Truncation lag parameter = 7, p-value = 0.1 Warning message: p-value greater than printed p-value in: kpss.test(s) My question is whether p=0.1 is a good number to reject N0? On the other hand, I have a

Hello everyone! Quite a similar question has been posed here some time ago, but there was no explicit solution offered. So I hope that it is OK to pose it again. I want to perform a KPSS test using the packages "urca" or "tseries". But I neither want to use the predefined lag structures, "short" and "long", nor specify the number of lags arbitrarily by

I'm trying to understand how to use kpss.test() properly. If I have a level stationary series like rnorm() in the help page, shouldn't I get a small p-value with the null hypothesis set to "Trend"? The (condensed) output from kpss.test() for the two possible null hypotheses is given below. I don't see any significant difference between these results. > x <-

Hi, Am I interpreting the results properly? Are my conclusions correct? > KPSS.test(df) ---- ---- KPSS test ---- ---- Null hypotheses: Level stationarity and stationarity around a linear trend. Alternative hypothesis: Unit root. ---- Statistic for the null hypothesis of level stationarity: 1.089 Critical values: 0.10 0.05 0.025 0.01 0.347 0.463

Hi, How can I access the Values stored in kpssstat-class given by KPSS.test function and store it in a variable. For example: >x <- rnorm(1000) >test <- KPSS.test(ts(x)) >test ---- ---- KPSS test ---- ---- Null hypotheses: Level stationarity and stationarity around a linear trend. Alternative hypothesis: Unit root. ---- Statistic for the null

Hi I am using the package urca and I am interested about the KPSS test. That works fine except the method "summary" did not work in the script, only when it is typed direct in the console the results are shown( not a source file). Is there any problem with these method ?

Hi All, First of all, could you tell me what the "KPSS Level" in the output of the test means? I have a series, x, of periodic data and tried kpss.test() on it to verify its stationarity. The tests gave me the p-value above 0.1. Since the null hypothesis N0 is that the series _is_ stationary, this means that I cannot reject N0. But the series does look periodic! So does all this

Dear R-Devel subscriber, I was hinted to the following problem with package 'urca': If one starts R and executes for instance: > library(urca) > example(ur.df) ## output as expected, but omitted here > class(lc.df) [1] "ur.df" attr(,"package") [1] "urca" > class(summary(lc.df)) [1] "sumurca" attr(,"package") [1]

Dear R-Devel subscriber, as a follow up to my yesterday's email: I tested an analogous example with the S4-package "flexclust" by executing the following code: library(flexclust) example(cclust) cl After saving the work space and starting a new R process with the restored work space, the same behaviour (i.e., the methods pertinent to "flexclust" are not used, even after

Sorry, to bother the list one more time: but the following worked at least for 'urca': in NAMESPACE I now included explicitly: import(methods) a fix of the 'urca'-package will be uploaded to CRAN on the weekend. Fritz, will this work for ypur package 'flexclust' too? I have in my DESCRIPTION imports: methods and in flexclust it is in depends: methods. However, both

Dear all, How to do ADF test ¡¢KPSS¡¢ PP¡¢GLS test in R£¿ Thanks a lot ! [[alternative HTML version deleted]]

Hi, I have been fitting a series of data representing a week of Internet traffic (which is daily seasonal and have a general trend toward lower rate at the weekends). Before I do the ARMA fit (which takes care of seasonality with a lag equal to one day), do I have to make sure the data is stationary? From the results and visually, it seems that this was taken care of. But the residual

Dear All, I am trying to use kpss.test function so as to perform a stationarity test on a data sample. Is it possible to know the associated confidence level for this test? I have not seen any arguments related to it. I had a look at some other tests included in R (adf.test, pp.test, ks.test ...) and I could not find this information for them. Thanks in advanced. Kind regards, Belén

Ukech U. Kidi wrote: > dax<- diff(log(DAX_CAC$DAX[1:1865])) > m1<- garch(dax) > Error: couldn't find function "garch" > m1<- garch(dax[1:1865]) > Error: couldn't find function "garch" > m1<- garch(dax[1:1865]) I am sorry, but I forgot to change the addres to r-help in the reply. Well, I am not sure, wheere do you want to get

If I try the breakpoints() function (strucchange package) with a minimum segment size = the number of regressors, there appears the following error message: "minimum segment size must be greater than the number of regressors" According to the documentation: "breakpoints implements the algorithm described in Bai & Perron (2003) for simultaneous estimation of multiple

On 03/01/2017 05:30 PM, Daniel Berlin via llvm-dev wrote: > So, https://bugs.llvm.org/show_bug.cgi?id=32056 is an example showing > our current TBAA tree for union generation is definitely irretrievably > broken. > I'll be honest here. I'm pretty sure your proposal doesn't go far enough. > But truthfully, I would rather see us come closer to a representation > we