The RATS Software Forum

I'm sorry. But I thought: when the explanatory variable isn't correlated with individuals effects it's better to use random effect. isn't that the definition of random effects model?
I'm sorry but I somehow confused.

I have an unbalanced sample. the demand surplus is a individual-specific value plus a second half shift that's common to all the commodities. Is that what you intend? Yes, that is exactly what I intend. a difference means that there is some correlation between the individual effect and the number of...

Thank you very much for your kind reply. The results of random and fixed effects are not identical( I don't know why they should be identical and I would be grateful if you could possibly guide me). I just wanted your insightful guide about my model. I don't know if it is possible to estimate a fixe...

Hi Tom, I want to examine the effects of the lifting a price ceiling on the demand surplus for a large number of commodities ( two groups). I have the data of weekly demand surplus for 370 commodities (20 weeks ). After the tenth week, the price controls have been lifted and so I introduced a dummy ...

Usable Observations 478 Log Likelihood -905.3081 Variable Coeff Std Error T-Stat Signif ************************************************************************************ Mean Model(DLX) 1. DLX{1} -0.469852739 0.072277425 -6.50068 0.00000000 2. DLX{2} -0.042194519 0.071854942 -0.58722 0.55705727 ...

Something that is really strange is the changing of results from bekk to cc model: In bekk model A(1,2) is statistically significant meaning that residuals of first variable (futures market) has a positive effect on volatility of the other variable (spot market) not vice versa. But in the cc model i...

Thank you very much for your kind relply; I don't know why univariate arch test cannot reject heteroscedasticity but multivariate arch test rejects heteroscedasticity (bekk model): Independence Tests for Series Z1 Test Statistic P-Value Ljung-Box Q(10) 14.552596 0.1492 McLeod-Li(10) 27.750828 0.0020...

Thank you very much for the insightful and informative guidance you gave me here in RATS forums. I learned so much from you. I just excluded first half of the data set and this is the final results from bekk vecm: MV-GARCH, BEKK - Estimation by BFGS Convergence in 79 Iterations. Final criterion was...

So, I learned another valuable lesson from you Tom:
If in the second subsample larger correlations suddenly become zero or if the don't follow a systematic trend, we can conclude that we don't need to be worry about them. Am I right?

I estimated the model over the full sample (500 observations). Here is autocorrelations of first subsample: Autocorrelations 1 2 3 4 5 6 7 8 9 10 0.05262 -0.00208 -0.01121 0.00413 -0.01347 0.10828 -0.08242 0.08748 -0.05907 -0.05012 11 12 13 14 15 16 17 18 19 20 -0.06336 -0.04190 0.01328 0.07142 -0.0...

Thank you very much for the insightful and informative guidance you gave me here in RATS forums. I learned so much from you. I just excluded first half of the data set and this is the final results from bekk vecm: MV-GARCH, BEKK - Estimation by BFGS Convergence in 79 Iterations. Final criterion was ...

Dear Tom, I want to estimate a bivariate VAR model. AIC criterion selects 3 lags while GTOS selects 19 lags (!!). I estimate model with 3 lags. @MVQstat(lags=5) shows that there are not significant correlations between residuals. Results: Multivariate Q(5)= 9.94324 Significance Level as Chi-Squared(...

Thank you very much. when we say that X and Y are cointegrated with DET=Trend , shouldn't we include trend in the calculation of ECT? I mean when we have this equation: x=trend+bY, then ECT should be ECT=trend+bY-X. But in the following code it seems that we don't include trend in calculation of ECT...

Dear Tom, suppose we have two variables "X" and "Y". And suppose @Johmle shows that they are cointegrated with DET=Trend. Now, I am not sure how to calculate ECT term in VECM. Is the below code correct?: * estimating a VECM model linreg(define=cointeq) X # Y system(model=mvmean) ...

Thank you for your help.
Do you mean I should remove outliers from the data?