[R] Different ARCH results in R and Eviews using garch from tseries
Constantine Tsardounis
costas.magnuse at gmail.com
Sun Dec 25 23:25:32 CET 2005
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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