[R] Different ARCH results in R and Eviews using garch from tseries

From: Constantine Tsardounis <costas.magnuse_at_gmail.com>
Date: Mon 26 Dec 2005 - 09:25:32 EST


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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