To whom it may concern, The estimate I got for EGARCH1 is too high (above 0.99) and it suggests that it may not be a global maximum, is there anyway to restrict the parameter to be something smaller than 0.99? This is the code I originally used: proc autoreg data=Finance_Modeling_Sample; model y = / garch=(p=1,q=1 , type = exp); hetero het1 het2; run; The result I got, with EGARCH1(0.9950) being too high. Intercept 1 1.6148 0.000540 2990.80 <.0001 EARCH0 1 0.000673 0.000261 2.58 0.0099 EARCH1 1 0.0736 0.000454 162.14 <.0001 EGARCH1 1 0.9950 0.0000691 14401.0 <.0001 THETA 1 0.0166 0.002381 6.97 <.0001 HET1 1 0.004952 0.002694 1.84 0.0661 HET2 1 5.8208 0.6125 9.50 <.0001 So I added the restrict statement below, proc autoreg data=Data; model y = / garch=(p=1,q=1 , type = exp); hetero het1 het2; restrict Egarch1<=0.98; run; But it seems HET1 is affected and I'm not sure why t value is infinity. Something doesn't seem right. Did I use the restrict statement correctly? Intercept 1 1.6173 0.000583 2776.25 <.0001 EARCH0 1 -0.0214 0.000477 -44.90 <.0001 EARCH1 1 0.1493 0.000776 192.29 <.0001 EGARCH1 1 0.9800 0 Infty <.0001 THETA 1 -0.0503 0.002424 -20.77 <.0001 HET1 1 0 0 . . HET2 1 37.2072 1.3713 27.13 <.0001 Restrict1 -1 95.7594 49.6844 1.93 0.0539 Restrict2 -1 39224 540.4422 72.58 <.0001 I only had one restrict statement, how come there are two restrict parameters in the table above? I also tried to hard-coded following an example online: PROC MODEL DATA=Data; label ro='x1 parameter' cv='x2 parameter' tb='x3 parameter' tp='x4 parameter' Dm='x5 parameter' ro2='het1 parameter' cv2='het2 parameter'; /*Mean model*/ log_yield = intercept+ro*x1+cv*x2+tb*x3+tp*x4+dm*x5; /*Variance Model*/ if (_obs_=1) then h.y=exp(earch0+egarch1*log(mse.y))+ro2*x1+CV2*x2; else h.y=exp(earch0+earch1*zlag(g) +egarch1*log(zlag(h.y)))+ro2*het1+CV2*het2; g=- theta*nresid.y+abs(nresid.y) - sqrt(2/CONSTANT('pi')) ; fit y/method=marquardt fiml; run; However, I got the following error message. My data set is mostly populated and what would you advise me to do to fix this? A total of 87930 errors occurred for 87930 observations during execution of the model program. These errors may produce missing valued results, which may cause the observations to be omitted. At FIML Iteration 0, with the parameter values listed below, there are 0 nonmissing observations available. At least 18 are needed to compute the next iteration. Note: Check the starting values used for the parameter estimates. Invalid starting values can cause errors which produce missing results, and prevent the iterative estimation process from proceeding. Thank you, in advance, for taking your time to read my question!
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