similar to: adf.test help

Hi All, I'm new to R and am trying to run a unit root test on the vector "y" (a time series of inflation (i.e. changes in the Consumer Price Index quarter on quarter)). I've run the Augmented-Dickey-Fuller Test below (R's URCA package). It gives me an error that it cannot find the function ur.df unless I comment out the third last line of code (see below). I try to call

Hi, While doing the ADF test in R using the following command I am getting the error and the result.."> x.ct=ur.df(rev$REVENUE,start=1,end=length(rev$REVENUE),frequency=1) Error in ur.df(rev$REVENUE, start = 1, end = length(rev$REVENUE), frequency = 1) : unused argument(s) (start = 1, end = 4, frequency = 1) >

Hello, I am using the ur.df function from the {arca} package to run the augmented Dickey-Fuller unit root test on several time series. However; I do not understand the econometric interpretation of the the "phi1" and "phi2" test-statisitc which are output if you choose a "trend" or "drift" model. I looked at the source code for the function but I do not

I have a question regarding the adf.test command in the tseries library. I have a vector of time series observations (2265 daily log prices for the OEX to be exact). I also have this same data in first-differenced form. I want to test both vectors individually for staionarity with an Augmented Dickey-Fuller test. I noticed when I use the adf.test command from the tseries library, the general

Hello, I have a question on unit root test with urca toolbox. First, to run a unit root test with lags selected by BIC, I type: > CPILD4UR<-ur.df(x1$CPILD4[5:nr1], type ="drift", lags=12, selectlags ="BIC") > summary(CPILD4UR) The results indicate that the optimal lags selected by BIC is 4. Then I run the same unit root test with drift and 4 lags:

Hi all, I was running the adf test in R. CODE 1: adf.test(data$LOSS) Augmented Dickey-Fuller Test data: data$LOSS Dickey-Fuller = -1.9864, Lag order = 2, p-value = 0.5775 alternative hypothesis: stationary CODE 2: adf.test(diff(diff(data$LOSS))) Augmented Dickey-Fuller Test data: diff(diff(data$LOSS)) Dickey-Fuller = -6.9287, Lag order = 2, p-value = 0.01 alternative

Hi all, Hope you people do not feel irritated for repeatedly sending mail on Time series. Here I got another problem on the same, and hope I would get some answer from you. I have following dataset: data[,1] [1] 4.96 4.95 4.96 4.96 4.97 4.97 4.97 4.97 4.97 4.98 4.98 4.98 4.98 4.98 4.99 4.99 5.00 5.01 [19] 5.01 5.00 5.01 5.01 5.01 5.01 5.02 5.01 5.02 5.02 5.03 5.03 5.03

Dear sir, I am a new user of R statistical package. I want to perform adf.test(augmented dickey fuller test), which packages I need to install in order to perform it. I am getting following message on my monitor. *x<-rnorm(1000) > adf.test(x) Error: could not find function "adf.test" *I am waiting for your response. Kamlesh Kumar. -- Kamlesh Kumar Appt. No. - QQ420,

Hello all, I tried to extract the p-value from adf.test in tseries; however, I got the error message such as > ht=adf.test(list.var$aa) > ht$p-value Error in ht$p - value : non-numeric argument to binary operator > ht Augmented Dickey-Fuller Test data: list.var$aa Dickey-Fuller = -2.3147, Lag order = 4, p-value = 0.4461 alternative hypothesis: stationary > ht$data [1]

Dear List, I have multiple time series, all of which (excepting 1) have missing values. These run for ~30 years, with monthly sampling. I need to determine stationarity, and have tried to use the Augmented Dickey-Fuller test (adf.test), but this cannot handle missing values. The same problem occurs when attempting cross-correlation (ccf). Could someone please suggest any suitable functions in

Dear Users, please help with the following DF test: ===== library(tseries) library(timeSeries) Y=c(3519,3803,4332,4251,4661,4811,4448,4451,4343,4067,4001,3934,3652,3768 ,4082,4101,4628,4898,4476,4728,4458,4004,4095,4056,3641,3966,4417,4367 ,4821,5190,4638,4904,4528,4383,4339,4327,3856,4072,4563,4561,4984,5316 ,4843,5383,4889,4681,4466,4463,4217,4322,4779,4988,5383,5591,5322,5404

I am confused by obtaining different results when testing for unit root when using different packages. I have 2625 price entries for which I want to determine whether they exhibit unit root. First I test using adf.test from tseries package by running: > adf.test(P, k=30) Augmented Dickey-Fuller Test data: P Dickey-Fuller = -4.685, Lag order = 30, p-value = 0.01 alternative hypothesis:

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 am applying the augmented-Dickey-Fuller Unit Root Test (ur.df function of the urca package) to a time series of approximately 50 values. To be sure I understood what was going on with the ur.df function, I checked the critical values of the 3 test statistics (tau, phi2 and phi3 if a trend is included) or the 2 test statistics (tau and phi1 if only a drift is included) with the

Short question: Is it possible to use statistical tests, like the Augmented Dickey-Fuller test, in functions with for-loops? If not, are there any alternative ways to scale measures? Detailed explanation: I am working with time-series, and I want to flag curves that are not stationary and which display pulses, trends, or level shifts. >df DATE ID VALUE2012-03-06 1

Hi all, I tried to do a Dickey Fuller test with R using adf.test with a time series of german stock prices. I have 10 stocks from 1985 to 2009 with monthly stock prices. So if you do the math I have 289 values for each stock. I tried to do the test for each stock alone and had the 289 values of my first stock listed in R. When I tried to do the test with command adf.test(x, k=1) the following

R has a Dickey-Fuller Test implementation (adf.test) that tests for unit roots in an autoregressive process with a constant and linear trend. Is there a DF implementation that doesn't use the constant or trend? Thanks, Jake. -- View this message in context: http://www.nabble.com/Dickey-Fuller-Tests-with-no-constant-and-no-trend-tp23565210p23565210.html Sent from the R help mailing list

I am not able to run Dickey-Fuller test. adf.test() function is not working. It is showing 'Error: could not find function "adf.test" Can any tell how to call "time series" library? -- Siddharth Arun, 4th Year Undergraduate student Industrial Engineering and Management, IIT Kharagpur [[alternative HTML version deleted]]

Hi, I'm using the ADF.test function in the uroot package to obtain an adfstat-class object. I'm wondering how I can extract the values (test statistic, p value, etc.) from this class, since it doesn't seem to have usual values. I get the following summary, but I'm not sure how to do anything with these values -- how can I put the number into another variable? --------- ------ -

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)