search for: kpss

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 s...

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 0.4...

...tive = "stationary") > Box.test(melbmax) Box-Pierce test data: melbmax X-squared = 1893.093, df = 1, p-value < 2.2e-16 > Box.test(melbmax,type="Ljung-Box") Box-Ljung test data: melbmax X-squared = 1894.650, df = 1, p-value < 2.2e-16 > kpss.test(melbmax) KPSS Test for Level Stationarity data: melbmax KPSS Level = 0.1163, Truncation lag parameter = 13, p-value = 0.1 Warning message: p-value greater than printed p-value in: kpss.test(melbmax) > x=time(melbmax) > y=as.vector(melbmax) > melbmaxsaz=lowess(x,y,f=0.0...

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...

...ay 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) #---Augmented Dickey-Fuller adf.test(var, alternative = "stationary", k = trunc((length(var)-1)^(1/3))) #---KPSS kpss.test(var, null = "Level", lshort = TRUE) kpss.test(var, null = "Trend", 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,...

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...

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 hypot...

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 myself. Unfortunately there is, or I have not found it yet, no possibility of an automatic...

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 informati...

...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 failed the kpss test with a p-value less than 0.01. Thank you, Weiguang

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

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 ?

...duals show-methods Methods for Function show in Package 'urca' summary-methods Methods for Function summary in Package 'urca' ur.ers Elliott, Rothenberg & Stock Unit Root Test ur.ers-class Representation of class 'ur.ers' ur.kpss Kwiatkowski et al. Unit Root Test ur.kpss-class Representation of class 'ur.kpss' ur.pp Phillips & Perron Unit Root Test ur.pp-class Representation of class 'ur.pp' ur.sp Schmidt & Phillips Unit Root T...

...), then I sub it into different unit root tests. I got different results among the tests. The following are the test statistics I got: adf.test @ tseries ~ -10.2214 (lag = 9) ur.df @ urca ~ -21.8978 ur.sp @ urca ~ -27.68 pp.test @ tseries ~ -972.3343 (truncation lag =7) ur.pp @ urca ~ -973.2409 ur.kpss @ urca ~ 0.1867 kpss.test @ tseries ~ 0.1867 (truncation lag =7) Questions: 1. Why there are different test statistics? Even in tests under same test name, say Phillips-perron test (pp.test & ur.pp), they have different test statistics. 2. Don't the Phillips-perron test based on the Dickey...

...t;> class(summary(lc.df)) >>[1] "table" >> >> >>In the NAMESPACE file export directives for methods and classes are >>included as: >> >>## Classes >>exportClasses("urca", "ca.jo", "cajo.test", "ur.kpss", "ca.po", >>"ur.ers", "ur.pp", "ur.sp", "ur.df", "ur.za", "sumurca") >>## Methods >>exportMethods("show", "plot", "summary") >> >>Any help or pointers for solving...

...utput. >## similarily > >> class(summary(lc.df)) >[1] "table" > > >In the NAMESPACE file export directives for methods and classes are >included as: > >## Classes >exportClasses("urca", "ca.jo", "cajo.test", "ur.kpss", "ca.po", >"ur.ers", "ur.pp", "ur.sp", "ur.df", "ur.za", "sumurca") >## Methods >exportMethods("show", "plot", "summary") > >Any help or pointers for solving this problem is much...

...ook like that: library(tseries) #import data from csv file with first row Open, High, Low, Close, Volume wig20 <- read.csv("wig20.txt", sep=";", dec=",") #multiply by 100, because sometimes it's easier to converge the model r <- 100*diff(log(wig20$CLOSE)) kpss.test(r) pp.test(r) acf(r) pacf(r) #if there is no significant autocorelation: y <- r - mean(r) fit <- garch(y, order = c(1,1)) summary(fit) plot(fit) #If you need some particular results for further testing, then use: ch <- predict(fit, genuine=TRUE) e <- fit$residuals #end do what...

...## still the same as compared to the above output. ## similarily > class(summary(lc.df)) [1] "table" In the NAMESPACE file export directives for methods and classes are included as: ## Classes exportClasses("urca", "ca.jo", "cajo.test", "ur.kpss", "ca.po", "ur.ers", "ur.pp", "ur.sp", "ur.df", "ur.za", "sumurca") ## Methods exportMethods("show", "plot", "summary") Any help or pointers for solving this problem is much appreciated. Best,...

Hi there, Does anyone know if R has a function for testing whether a time series is stationary?? Thanks in advance, Wayne Dr Wayne R. Jones Statistician / Research Analyst KSS Group plc St James's Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd A division of Knowledge Support Systems Group plc Seventh Floor St James's