similar to: normality tests

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

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

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?

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]]

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

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

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 .. 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

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))]))}

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

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

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

Dear Federico, see: ? shapiro.test(stats) Shapiro-Wilk Normality Test and ? jarque.bera.test(tseries) Jarque-Bera Test They are the most common tests used for normality testing. Ciao Vito Federico Gherardini wrote on Fri Oct 15 14:44:18 CEST 2004: Hi all, Is it possible to have a test value for assessing the normality of residuals from a linear regression model,

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 have compiled a package in R which performs the Doornik-Hansen (1994) version of the omnibus normality test (a finite sample version of the Jarque-Bera test), including a variation which allows for weak dependence rather than independence of the variable(s) in question. I have tried to contact the compiler of the "nortest" package (which deals with the same subject) but have not

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