From: oliver wee <islandboy1982_at_yahoo.com>

Date: Wed 15 Feb 2006 - 22:20:27 EST

Box.test(SP500DataSetFitMA2$residuals, type = "Ljung", lag =1)

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Feb 15 22:35:08 2006

Date: Wed 15 Feb 2006 - 22:20:27 EST

Hello, I am using the Ljung Box test in R to compute
if the resiudals of my fitted model is random or not.

I am not sure though what the results mean, I have looked at various sources on the internet and have come up with contrasting explanations (mainly because these info deal with different program languages, like SAS, SPSS, etc).

I know that my residuals should appropriate white noise( is random) since a check of its ACF shows it to be so (signifant correlation only at lag 1, decays very quickly to zero).

But I am not sure how to interpret the ljung-box result given by R.

To check for randomness of residuals, should the p-value be small or large? How small and how large? And at what lags should I check for the randomness of the residuals? Is a p-value > 0.05 (or < 0.05) enough? What if I have a very large p-value of 0.9796 at lag 1, but its value is 0.0139 at lag 8?

For example, here's what I got for the first 10 lags of the residuals I'm testing:

Box.test(SP500DataSetFitMA2$residuals, type = "Ljung", lag =1)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 7e-04, df = 1, p-value = 0.9796

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =2)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 0.1088, df = 2, p-value = 0.947

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =3)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 1.4179, df = 3, p-value = 0.7014

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =4)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 3.866, df = 4, p-value = 0.4244

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =5)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 6.0251, df = 5, p-value = 0.3038

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =6)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 12.11, df = 6, p-value = 0.05956

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =7)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 13.0307, df = 7, p-value = 0.07137

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =8)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 19.1766, df = 8, p-value = 0.01394

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =9)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 19.6753, df = 9, p-value = 0.02003

> Box.test(SP500DataSetFitMA2$residuals, type =

"Ljung", lag =10)

Box-Ljung test

data: SP500DataSetFitMA2$residuals

X-squared = 19.7124, df = 10, p-value = 0.03209

I know this is not really a programming question, so I apologize if it is inappropriate or if the question is too elementary.

Thank you very much for your help.

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Feb 15 22:35:08 2006

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