[R] Degrees of freedom using Box.test()

Patrick Burns pburns at pburns.seanet.com
Wed Mar 8 17:22:17 CET 2006


In 3 decades I suspect that the definition of "large"
has changed.  Now with faster computers and R
we don't need to rely on the guesses of Ljung and
Box.  All it will take is someone (not me) to do
some experiments.

Pat

Nestor Arguea wrote:

>It's Ljung and Box (1978) saying that for large number of observations a chi 
>squared with lags-p-q, should provide a good approximation "for most 
>practical purposes" (p. 298 of reference above).
>
>Nestor
>On Wednesday 08 March 2006 4:00 am, Patrick Burns wrote:
>  
>
>>You are saying that the penalty on the degrees of freedom
>>should be the same whether the model was fit with 100
>>observations or 1 million observations.  You are also saying
>>that some tests should have negative degrees of freedom.
>>So I don't think your proposal is the right answer, though
>>presumably there should be some penalty.
>>
>>There is a working paper on the Burns Statistics website
>>about robustness in Ljung-Box tests, but this issue is not one
>>that is covered.
>>
>>
>>Patrick Burns
>>patrick at burns-stat.com
>>+44 (0)20 8525 0696
>>http://www.burns-stat.com
>>(home of S Poetry and "A Guide for the Unwilling S User")
>>
>>Nestor Arguea wrote:
>>    
>>
>>>After an RSiteSeach("Box.test") I found some discussion regarding the
>>>degrees of freedom in the computation of the Ljung-Box test using
>>>Box.test(), but did not find any posting about the proper degrees of
>>>freedom.
>>>
>>>Box.test() uses "lag=number" as the degrees of freedom.  However, I
>>>believe the correct degrees of freedom should be "number-p-q" where p and
>>>q are the number of estimated parameters (for instance, in a Box-Jenkins
>>>family of models). This, according to the main source in documentation of
>>>Box.test:
>>>
>>>G. M. Ljung and G. E. P. Box, On a measure of Lack of Fit in Time Series
>>>Models, Biometrika, Vol. 65, No. 2 (August, 1978), pp. 297-303.
>>>
>>>One can still compute the correct p-value with
>>>
>>>      
>>>
>>>>1-pchisq(value,correctdf)
>>>>        
>>>>
>>>Nestor
>>>(R 2.2.1 on Linux, Suse 9.3)
>>>      
>>>
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>>    
>>
>
>  
>




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