OK, it's no good. Here is the result:
> data(m.intc7303)
> archTest(log(1+as.numeric(m.intc7303)), lags=12)
ARCH test (univariate)
data: Residual of y1 equation
Chi-squared = 13.1483, df = 12, p-value = 0.3584
On 2/2/08, tom soyer <[EMAIL PROTECTED]> wrote:
>
> Spencer,
>
> Sorry, I forgot that the default lag in arch is 16. Here is the fix. Can
> you try it again and see if it gives the correct (or at least similar
> compared to a true LM test) result?
>
> archTest=function(x, lags=12){
> #x is a vector
> require(vars)
> s=embed(x,lags)
> y=VAR(s,p=1,type="const")
> result=arch(y,lags.single=lags,multi=F)$arch.uni[[1]]
> return(result)
> }
>
> Thanks and sorry about the bug.
>
>
> On 2/2/08, Spencer Graves <[EMAIL PROTECTED]> wrote:
> >
> > Dear Tom, Bernhard, Ruey:
> >
> > I can't get that to match Tsay's example, but I have other
> > questions about that.
> >
> > 1. I got the following using Tom's 'archTest' function (below):
> >
> > > archTest(log(1+as.numeric(m.intc7303)), lags=12)
> >
> > ARCH test (univariate)
> >
> > data: Residual of y1 equation
> > Chi-squared = 10.8562, df = 16, p-value = 0.8183
> >
> > Warning message:
> > In VAR(s, p = 1, type = "const") :
> > No column names supplied in y, using: y1, y2, y3, y4, y5, y6, y7, y8,
> > y9, y10, y11, y12 , instead.
> >
> > >
> > ** First note that the answer has df = 16, even though I
> > supplied lags = 12.
> >
> > 2. For (apparently) this example, S-Plus FinMetrics 'archTest'
> > function returned "Test for ARCH Effects: LM Test. Null Hypothesis:
> > no ARCH effects. Test Stat 43.5041, p.value 0.0000. Dist. under Null:
> > chi-square with 12 degrees of freedom".
> >
> > 3. Starting on p. 101, Ruey mentioned "the Lagrange multiplier
> > test of Engle (1982)", saying "This test is equivalent to the usual F
> > test for" no regression, but refers it to a chi-square, not an F
> > distribution. Clearly, there is a gap here, because the expected value
> > of the F distribution is close to 1 [d2/(d2-2), where d2 = denominator
> > degrees of freedom; http://en.wikipedia.org/wiki/F-distribution], while
> > the expected value for a chi-square is the number of degrees of freedom
> >
> > Unfortunately, I don't feel I can afford the time to dig into this
> > further right now.
> >
> > Thanks for your help.
> > Spencer Graves
> >
> > tom soyer wrote:
> > > Spencer, how about something like this:
> > >
> > > archTest=function (x, lags= 16){
> > > #x is a vector
> > > require(vars)
> > > s=embed(x,lags)
> > > y=VAR(s,p=1,type="const")
> > > result=arch(y,multi=F)$arch.uni[[1]]
> > > return(result)
> > > }
> > >
> > > can you, or maybe Bernhard, check and see whether this function gives
> > > the correct result?
> > >
> > > thanks,
> > >
> > > On 2/1/08, *Spencer Graves* <[EMAIL PROTECTED]
> > > <mailto:[EMAIL PROTECTED]>> wrote:
> > >
> > > Hi, Tom:
> > >
> > > The 'arch' function in the 'vars' package is supposed to be
> > able
> > > to do that. Unfortunately, I was unable to make it work for a
> > > univariate series. Bernhard Pfaff, the author of 'vars', said
> > > that if I
> > > read the code for 'arch', I could easily retrieve the necessary
> > lines
> > > and put them in my own function; I have not so far found the time
> > to
> > > try that. If you do, or if you get a better answer than this,
> > > would you
> > > please let me know? I would like to have this capability for the
> > > 'FinTS' package, and I would happily write a help page if someone
> > > would
> > > contribute the function -- or use a function in another
> > package. Tsay
> > > (2005) Analysis of Financial Time Series, 2nd ed. (Wiley) includes
> > an
> > > example on p. 103 that could be used for a reference.
> > >
> > > Hope this helps.
> > > Spencer Graves
> > >
> > > tom soyer wrote:
> > > > Hi,
> > > >
> > > > Does anyone know if R has a Lagrange multiplier (LM) test for
> > ARCH
> > > > effects for univariant time series?
> > > >
> > > > Thanks!
> > > >
> > > >
> > >
> > >
> > >
> > >
> > > --
> > > Tom
> >
>
>
>
> --
> Tom
--
Tom
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