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From |
Erkal Ersoy <erkal6@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: A univariate GARCH model |

Date |
Mon, 25 Jul 2011 17:45:54 +0100 |

Hello Statalisters, I am having trouble estimating a model in which I am using a GARCH(1,1) process to compute the conditional variance of the error term. The model can be summarized as follows (I use underscore "_" to denote a subscript): z_t = a + b1*z_1 + b2*z_2 + b3*z_3 + b4*z_4 + e_t.... (1) where e_t | I_t ~ N(0, h_t) and I_t is the information set available at time t. Also, e(hat) = z_t - z_t(hat) ..........................................(2) and lastly, h_t = c0 + c1*e^2_(t-1) + c2*h_(t-1) ............................(3) What I would like to do now is construct a normalized variable, e(star), as follows: e(star)= e(hat) / sqrt(h_t) .............................................(4) In order to get here, I need to estimate the coefficients c0, c1 and c2. Obtaining the residuals in equation (1) is no problem of course. Once I have done that, however, I am having a difficult time isolating the variance, h_t, so that I can regress it on e(hat)-squared and the first lag of h_t. Is there a way to do something like this? I also took a look at Stata's dvech command, but I'm not entirely sure that's useful here. I would appreciate any help very much. Thank you all in advance for your time and help. Best, Erkal * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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