similar to: ADF test

Hi, I am quite new to R and would appreciate some guidance, if possible. I have imported a csv file: spread <- read.csv("Spread.csv") I get the following error when I try to run adf.test: > adf.test(spread,alternative = c("stationary", "explosive"),0) Error in embed(y, k) : 'x' is not a vector or matrix Why is this? -- View this message in context:

this command is used in tseries adf.test(x, alternative = c("stationary", "explosive"), k = trunc((length(x)-1)^(1/3))) this command apply adf test on data given in x .here general equatiuon that is, equation with constant and trend is used.if i did not include constant or trend in the equation and run the command then how i can run this command in tseries.

Hello everybody, I've got a matrix called EUROPEDATA and I want to calculate the adf test statistic (part of the tseries package) on a rolling basis for window my.win on each column; i.e. each column of EUROPEDATA represents a particular variable; for the first column I calculate the adf test statistic for window my.win = 60 for example, roll forward one observation, calculate the adf

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,

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, I am trying to test whether a series is return series stationary, but before proceeding I wanted to make sure I understand correctly how to use the adf.test function and interpret its output... Could you please let me know whether I am correct in my interpretations? ex: I take x such as I know it doesn't have a unit root, and is therefore stationary 1/ > x <- rnorm(1000) >

Hello! I am applying the ADF.test function from package uroot to a time series of data. When I apply the full test, incorporating drift and trend terms, the regressor estimate of the drift term is not significantly different from zero. So I apply the test to a model without drift term, with deterministic trend only. But then I always get the following error:

Hello! I do not understand what is meant by: "aic" and "bic" follow a top-down strategy based on the Akaike's and Schwarz's information criteria in the datails to the ADF.test function. What does a "top-down strategy" mean? Probably the respective criterion is minimized and the mode vector contains the lag orders at which the criterion attains it

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

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

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

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

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]

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

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-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi! For some months now I'm hunting a Samba performance problem without a solution yet. Now I'm hoping someone on this list has an idea (In fact, I already reported the problem to the mailing list but got only one reply which did not help) So I'm here for another try... Here's the situation: Some of our users runs Windows XP with

Have you checked the network end of things? This is the same type of thing I see when the NIC and ethernet switch don't autonegotiate to the same settings. Hope I didn't step on anybody's toes here. I'm new to the list. --------------------------------------------------------------------------- Tony Hoover, Network Administrator KSU - Salina, College of Technology and

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Hi all R-users, I?m trying to assign colors on those p-value in my table output that fall above a certain critical value, let?s say a p-value >0.05. My table looks like this: Assets ADF-Level P-Value ADF-First D P-Value ADF-Second D P-Value [1,] Liabilities -2.3109 0.1988 -3.162 0.025 -6.0281

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