HenleyPhD wrote:Hey, thanks for this code. I have been just been using it to produce some results for some ADCC and AGDCC models. I was wondering if anyone could help me understand something.
When I run a 3/ 4 time series model I can understand all the results for all four techniques but when I run it for only 2 series I get 6 coefficients for example alpha 1 and alpha 2 using AGDCC. Looking at the original article I can understand ADCC as a model. However; I am not sure the generalization’s impact and why that leads to the two coefficients for each parameter being produced. How do I interpret these? Or alternatively is the point that I should not be using the generalized techniques when looking at the relationship between only two series?
maximize(start=%(StartQC()),pmethod=simplex,piters=5,method=bfgs,title="GDDCC") logl gstart gend
disp "GDDCC BIC" -2.0*%logl+(%nreg+uniparms)*log(%nobs)
* AGDDCC (Asymmetric Generalized Diagonal DCC)
nonlin aq bq gq
maximize(start=%(StartQC()),pmethod=simplex,piters=5,method=bfgs,title="AGDDCC") logl gstart gend
disp "AGDDCC BIC" -2.0*%logl+(%nreg+uniparms)*log(%nobs)
Thank you, those options for the MAXIMIZE instruction did help for a lot of my models.
Could you possibility help me understand two others things? If I try and run the code for shorter data series (300/ 400 obvs) I find that the results are very sensitive to the initial values specified in the code. This does make sense to me; would it then be correct to base the starting values on the univariate GARCH results? In this particular case these tend to lower than with most of the other models that I have run.
Also in one of the models I have tried I end up with one negative alpha value, is that wrong? Should I try and re-run it using alternative options?
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