Have you tried the garch modeling in the fSeries package?
Also, have you tried to think of an example so small and simple you
can work it yourself either entirely by hand or using something more
transparent? For example, I often use the "Solver" in Excel to minimize
a log(likelihood). When I can work a problem that way and then get the
same answer from R code, it increases my confidence that I know what R
is doing. (If you have Excel but have never used the Solver, you may
need to look first at Tools -> Add-Ins -> Solver. Then "Tools ->
Solver" should give it to you.) Or write a function to compute the
negative of the log(likelihood) and use optim to minimize it, with
hessian = TRUE to get the observed information, whose inverse is the
variance for the Wald approximation.
hope this helps.
spencer graves
Constantine Tsardounis wrote:
> Dear Sir,
>
> First of all Happy Holidays!,...
>
> I am writing to you because I am a bit confused about ARCH estimation.
> Is there a way to find what garch() exactly does, without the need of
> reading the source code (because I cannot understand it)?
> In Eviews (the results at the end) I am getting different results than
> in R (for those that have the program I do: Quick -> Estimage Equation
> -> Method: ARCH -> y c x -> GARCH:0 & ARCH:1 -> ARCH-M term: none.
>
> Data can be downloaded from
> http://constantine.evangelopoulos.com/1.2.2-askhseis.econometrix.csv
> and can be loaded in R with:
>
> x <- ts(read.csv("1.2.2-askhseis.econometrix.csv")[ ,1])
> y <- ts(read.csv("1.2.2-askhseis.econometrix.csv")[ ,2])
> garch(summary(lm(y ~ x))$resid^2, c(0,1))
>
> What I am doing wrong? Because I want to check for ARCH(q) effect and
> then estimate the final equations (Y on X, with the equation of the
> error term)
>
>
>
> Thank very much in advance for your assistance,
>
> Tsardounis Constantine
> Student in Economics at University of Thessaly, Greece
>
>
> Eviews results:
> Dependent Variable: Y
> Method: ML - ARCH
> Date: 12/26/05 Time: 00:05
> Sample(adjusted): 1 83
> Included observations: 83 after adjusting endpoints
> Convergence achieved after 16 iterations
>
> Coefficient Std. Error z-Statistic Prob.
>
> C 0.005268 0.002442 2.157327 0.0310
> X 0.947425 0.024682 38.38587 0.0000
>
> Variance Equation
>
> C 0.000456 8.55E-05 5.333923 0.0000
> ARCH(1) -0.041617 0.117458 -0.354311 0.7231
>
> R-squared 0.941163 Mean dependent var 0.016895
> Adjusted R-squared 0.938928 S.D. dependent var 0.086783
> S.E. of regression 0.021446 Akaike info criterion
> -4.801068
> Sum squared resid 0.036336 Schwarz criterion
> -4.684498
> Log likelihood 203.2443 F-statistic 421.2279
> Durbin-Watson stat 1.503765 Prob(F-statistic) 0.000000
>
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--
Spencer Graves, PhD
Senior Development Engineer
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