similar to: How can I test if time series residuals' are uncorrelated ?

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

tseries_0.3-0 at CRAN now contains the following new features: NelPlo Nelson-Plosser Macroeconomic Time Series garch Fit GARCH Models to Time Series get.hist.quote Download Historical Finance Data jarque.bera.test Jarque-Bera Test na.remove NA Handling Routines for Time Series garch contains a GARCH estimation routine together

tseries_0.3-0 at CRAN now contains the following new features: NelPlo Nelson-Plosser Macroeconomic Time Series garch Fit GARCH Models to Time Series get.hist.quote Download Historical Finance Data jarque.bera.test Jarque-Bera Test na.remove NA Handling Routines for Time Series garch contains a GARCH estimation routine together

Hello, I'm so confused why I can't run Jarque-Bera test on my data. I have 9968 observation and I want to run Jarque-Bera test on them, but no matter how hard I am trying I can't get it work. please let me know what should I do. Best, Kiana [[alternative HTML version deleted]]

Folks, I'm a bit puzzled by the fact that if I generate 100,000 standard normal variates using rnorm() and perform the Jarque-Bera on the resulting vector, I get p-values that vary drastically from run to run. Is this expected? Surely the p-val should be close to 1 for each test? Are 100,000 variates sufficient for this test? Or is it that rnorm() is not a robust random number generator?

Hi! I got a loop where i print out the results of Jarque Bera tests, but I have to put, the p-values in a vector. Can you help me how to do it in an effective way and not just typing in the results to a vector? Thanks a lot, here is the code: for(i in 1:60){ print(jarque.bera.test(loghozamok[((20*(i-1))+1):(20*(i+11))]))}

If I have data that I feed into shapio.test and jarque.bera.test yet they seem to disagree. What do I use for a decision? For my data set I have p.value of 0.05496421 returned from the shapiro.test and 0.882027 returned from the jarque.bera.test. I have included the data set below. Thank you. Kevin "Category","Period","Residual" "CHILD HATS, WIGS &

Hi all, I used the function jarque.test (in the moments package) on my data set and I obtained something like this: Jarque-Bera Normality Test data: x JB = 4.8381, p-value = 0.089 alternative hypothesis: greater or Jarque-Bera Normality Test data: x JB = 2.6018, p-value = 0.2723 alternative hypothesis: greater I cannot understand this. Please, someone can help me? thank you

Hello R-Users, The following questions are not R-technical, but more of general statistical nature. 1. NORMALITY I built a normal linear regression model and now I want to check for the residual normality assumption. If I check the distribution graphically and look at the descriptive characteristics (skewness and kurtosis are below 1), I would confirm that the residuals are normally

Hi all .. there is no doubt a simple answer to this, but it eludes me. In the first session below ( with jarque.bera.test) you will see that p.value prints with a name of X-squared . This is easily fixed by changing the source to assign a more appropriate name - no name is assigned in the source listing below (the original source code of jarque.bera.test() from tseries).. but what I

Dear all, i have the question about the using of Jarque-Bera Test by using R. The question is that I do not have in my package "ts" this test and can not obtain any information in the help-file. Could you help my? Where could I download the package and which one, to use the Jarque-Bera Test? Thank You, Susan --------------------------------- - New people, new

Hi all, apologies for seeking advice on a general stats question. I ve run normality tests using 8 different methods: - Lilliefors - Shapiro-Wilk - Robust Jarque Bera - Jarque Bera - Anderson-Darling - Pearson chi-square - Cramer-von Mises - Shapiro-Francia All show that the null hypothesis that the data come from a normal distro cannot be rejected. Great. However, I don't think it looks

Dear List: I am trying to understand how to use the jarque.bera.test() function of the "tseries" package. A numeric vector or time series seems to be the only argument required. What is the default significance level for rejecting the null of normality? Is there a way to specify different significance levels? platform i386-pc-mingw32 arch i386 os mingw32

Hi! I know that there is function in fBasics package for univariate Jarque-Bera test and a funtion for univariate Ljung-Box test in stats package. But I am wondering if there is a function somewhere to do the tests for multivariate time series? Thanks, John [[alternative HTML version deleted]]

Dear all R-users, I have a GARCH related query. Suppose I fit a GARCH(1,1) model on a dataframe dat >garch1 = garch(dat) >summary(garch1) Call: garch(x = dat) Model: GARCH(1,1) Residuals: Min 1Q Median 3Q Max -4.7278 -0.3240 0.0000 0.3107 12.3981 Coefficient(s): Estimate Std. Error t value Pr(>|t|) a0 1.212e-04 2.053e-06 59.05 <2e-16 *** a1

Dear all R-users, I have a GARCH related query. Suppose I fit a GARCH(1,1) model on a dataframe dat >garch1 = garch(dat) >summary(garch1) Call: garch(x = dat) Model: GARCH(1,1) Residuals: Min 1Q Median 3Q Max -4.7278 -0.3240 0.0000 0.3107 12.3981 Coefficient(s): Estimate Std. Error t value Pr(>|t|) a0 1.212e-04 2.053e-06 59.05 <2e-16 *** a1

The normality of the residuals is important in the inference procedures for the classical linear regression model, and normality is very important in correlation analysis (second moment)... Washington S. Silva > Thank you all for your replies.... they have been more useful... well > in my case I have chosen to do some parametric tests (more precisely > correlation and linear regressions

Hello, I am analysing several samples whose sizes are from 9 to 110. I would like to test their distribution with R, whether they are normal or not. I wonder which test for normality from R should I use . Thank you for help. Samuel Samuel BERTRAND Doctorant Laboratoire de Biomecanique LBM - ENSAM - CNRS UMR 8005 151, bd de l'Hopital 75013 PARIS Tel. +33 (0) 1 44 24 64 53 Fax +33 (0) 1

Hi all I'm using the Box-Ljung test (from within R) to test if a time-series in independently distributed. 2 questions: 1) p-value returned by Box-Ljung: IF I want to test if the time-series is independant at say 0.05 sig-level (it means that prob of erroneously accepting that the time-series is independent is 0.05 right?) --> then do I consider time-series as "independant"