A Forum for RATS Software and Econometrics Discussions
https://estima.com/forum/
Code: Select all
cmom(corr)
# variables
compute %cmom=%cmom*sqrt(%nobs)
*
disp %cmomCode: Select all
compute n=2
*
* Define the return series
*
dec vect[series] r(n)
compute start=1, end=750
set r(1) = 100*log(fra/fra{1})
set r(2) = 100*log(ger/ger{1})
*
* Template for mean equation. This is a VAR(1)
*
equation meaneq *
# constant r(1){1} r(2){1}
Code: Select all
Variable Coeff Std Error T-Stat Signif
************************************************************************************
1. B(1)(1) 0.049233484 0.031080586 1.58406 0.11318028
2. B(1)(2) -0.029537994 0.038492409 -0.76737 0.44286042
3. B(1)(3) 0.003853134 0.035468663 0.10863 0.91349209
4. B(1)(4) -0.011483730 0.020734891 -0.55384 0.57969104
5. B(1)(5) -0.014403906 0.021419092 -0.67248 0.50127830
6. B(2)(1) 0.069453165 0.051687750 1.34371 0.17904332
7. B(2)(2) 0.168135839 0.047800982 3.51741 0.00043577
8. B(2)(3) 0.023231342 0.053341768 0.43552 0.66318590
9. B(2)(4) -0.015210762 0.037747528 -0.40296 0.68697730
10. B(2)(5) -0.010654883 0.035051930 -0.30397 0.76114754
11. A(1)(1) 0.002922546 0.008504550 0.34365 0.73111326
12. A(1)(2) 0.146621640 0.034872157 4.20455 0.00002616
13. A(1)(3) -0.009506499 0.023067570 -0.41212 0.68025492
14. A(2)(1) 0.037589459 0.016418051 2.28952 0.02204915
15. A(2)(2) 0.033118479 0.015117959 2.19067 0.02847558
16. A(2)(3) 0.103180374 0.038044773 2.71208 0.00668630
17. D(1) -1.332757517 0.398151163 -3.34737 0.00081584
18. D(2) -0.541204025 0.302098755 -1.79148 0.07321622
19. G(1) 0.947575690 0.009841957 96.27919 0.00000000
20. G(2) 0.952638476 0.019601947 48.59918 0.00000000
21. RR(1,1) 0.362829658 0.028700696 12.64184 0.00000000
Code: Select all
MAXIMIZE - Estimation by BFGS
Convergence in 27 Iterations. Final criterion was 0.0000095 <= 0.0000100
Usable Observations 746
Function Value -2340.3786
Variable Coeff Std Error T-Stat Signif
************************************************************************************
1. B(1)(1) 0.043866259 0.030047126 1.45992 0.14431336
2. B(1)(2) -0.028237331 0.032342501 -0.87307 0.38262384
3. B(1)(3) -0.009105750 0.019289191 -0.47206 0.63688047
4. B(2)(1) 0.068639510 0.052417456 1.30948 0.19037245
5. B(2)(2) 0.166320047 0.038417338 4.32930 0.00001496
6. B(2)(3) -0.012156675 0.013639290 -0.89130 0.37276922
7. A(1)(1) 0.003478053 0.009199481 0.37807 0.70537816
8. A(1)(2) 0.143448536 0.036151163 3.96802 0.00007247
9. A(1)(3) -0.006822461 0.024400134 -0.27961 0.77977863
10. A(2)(1) 0.037727871 0.015439911 2.44353 0.01454440
11. A(2)(2) 0.032639706 0.015393948 2.12029 0.03398120
12. A(2)(3) 0.103701708 0.037852261 2.73964 0.00615058
13. D(1) -1.375944773 0.412245282 -3.33768 0.00084480
14. D(2) -0.538287971 0.352336195 -1.52777 0.12657014
15. G(1) 0.947938662 0.011011507 86.08619 0.00000000
16. G(2) 0.952677816 0.018517041 51.44871 0.00000000
17. RR(1,1) 0.362829054 0.040470315 8.96531 0.00000000
The B's are the mean model coefficients. As it's written, B(x)(1) is the intercept. B(x)(2) is the coefficient on the lag of the 1st variable and B(x)(3) is on the second, so B(1)(3) is the spillover in the first equation and B(2)(2) is the spillover in the second.ibrahim wrote:Tom,
Please ignore the results at my last post. I solved the results error and got new results as seen below for a bivariate egarch model.
For this model, "B" coefficients reflect return spillover and "A" coefficients reflect volatility spillover, right!
And B(1)(2) means own lagged retuns effect and B(1)(3) means the other variables' lagged return effect, right!
I am confused about interpreting the results!
Could you please help me?
Thank you very much.
I'm not sure what you mean by "I specified different VAR models". Did you increase the number of lags? That would be the only thing likely to fix the Q statistic. And how significant is it?ibrahim wrote:Thank you so much Tom,
I really appreciate your help.
One more question about bivariate VAR(1) EGARCH results. After checking diagnostic tests, LBQ for u(1) is insignificant but LB-Q for u(2) is significant. Although I specified different VAR models, diagnostis test has never changed. Actually, I don't know what to do?
Thank you very much.
Ibrahim
Code: Select all
set z1 %regstart() %regend() = rd(t)(1)/sqrt(hh(t)(1,1))
set z2 %regstart() %regend() = rd(t)(2)/sqrt(hh(t)(2,2))
set z3 %regstart() %regend() = rd(t)(3)/sqrt(hh(t)(3,3))
set z4 %regstart() %regend() = rd(t)(4)/sqrt(hh(t)(4,4))
## MAT15. Subscripts Too Large or Non-Positive
Error was evaluating entry 15
Code: Select all
set z1 %regstart() %regend() = u(1)/sqrt(hh(t)(1,1))
set z2 %regstart() %regend() = u(2)/sqrt(hh(t)(2,2))
set z3 %regstart() %regend() = u(3)/sqrt(hh(t)(3,3))
set z4 %regstart() %regend() = u(4)/sqrt(hh(t)(4,4))