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 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
