HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Moderators: EViews Gareth, EViews Moderator
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Dear trubador
Very appreciated to have a look my code and file.
I know there are no data until 10/31/2005. I need to run many models with different dependent variables (y1). The length of series varies. Some y1 have a longer period, some have a shorter period. Perhaps I need to tidy the matrix of dependent and independent variables data series first.
I don't understand why need to define the sample. The y1 data in the workfile is the sample I refer to.
Very appreciated to have a look my code and file.
I know there are no data until 10/31/2005. I need to run many models with different dependent variables (y1). The length of series varies. Some y1 have a longer period, some have a shorter period. Perhaps I need to tidy the matrix of dependent and independent variables data series first.
I don't understand why need to define the sample. The y1 data in the workfile is the sample I refer to.
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
misscats wrote:I don't understand why need to define the sample. The y1 data in the workfile is the sample I refer to.
LogL object refers to the sample period of the workfile, not the series used in estimation. Therefore, each time you change your variables you may need to adjust the sample accordingly prior to estimation to make sure that variables contain no missing values in the defined period.
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Thanks trubador. I think that might be my problem is. I will work on that. Your guidance is very helpful.

 Posts: 7
 Joined: Wed Oct 15, 2014 5:46 pm
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Dear Trubador,
if i want to create four variate multivariate garch in mean model, what part of the program should i modify?
if i want to create four variate multivariate garch in mean model, what part of the program should i modify?
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Hi all,
Again, I got the same message even using Eviews6 for bivariate GARCH: "!MLOG@PI is not defined in LOGL=5*(!MLOG@PI+.......)"
________________
врати блиндирани врати входни врати интериорни врати
Again, I got the same message even using Eviews6 for bivariate GARCH: "!MLOG@PI is not defined in LOGL=5*(!MLOG@PI+.......)"
________________
врати блиндирани врати входни врати интериорни врати
Last edited by samonia on Sun Nov 30, 2014 5:58 am, edited 1 time in total.
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Because it shoud read !mlog2pi. And it should be defined prior to logl specification as follows:
Code: Select all
' constant adjustment for log likelihood
!mlog2pi = 2*log(2*@acos(1))
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Dear Trubador & other experts,
I have the Eviews 8.1 version, and request your feedback on following. Will really appreciate your help. Any directions you can provide will be of immense help.
I am trying to run a GARCHinmean model with the following equation:
rd+1 = ad + βdvWdthdt+1 + βdc (1wdt) hct+1 + θd1Edt + θd2Edt1 + Edt+1
OR, conditional return = constant + Coefficient*(weight)*(conditional variance) + Coefficient*(weight)*(conditional Covariance) + one period prior's error + two period prior's error
OR, Basically, my GARCHinmean regresses conditional expected excess returns (t+1) of S&P 500 Index against the following factors: 1) conditional variance of itself i.e. S&P 500 2) conditional covariance with another market, 3) its error from current period (t) and 4) its error from one period back (t1).
On Eviews, I did the following:
Quick> Equation Estimation > Specification> I chose ARCH under Estimation Settings >For ARCHM I pulled down and chose Variance > Now I'm stuck what to put under "Mean Equation" and "Variance Regressors".
Should I also be checking the box on "Options" for "Coefficient Covariance" ?
Since this is a completely "conditional" model, I am a bit lost with the specifications....Again, being a novice with Eviews and Econometrics, any help will be greatly appreciated.
Thanks again,
Best Regards,
Srana
I have the Eviews 8.1 version, and request your feedback on following. Will really appreciate your help. Any directions you can provide will be of immense help.
I am trying to run a GARCHinmean model with the following equation:
rd+1 = ad + βdvWdthdt+1 + βdc (1wdt) hct+1 + θd1Edt + θd2Edt1 + Edt+1
OR, conditional return = constant + Coefficient*(weight)*(conditional variance) + Coefficient*(weight)*(conditional Covariance) + one period prior's error + two period prior's error
OR, Basically, my GARCHinmean regresses conditional expected excess returns (t+1) of S&P 500 Index against the following factors: 1) conditional variance of itself i.e. S&P 500 2) conditional covariance with another market, 3) its error from current period (t) and 4) its error from one period back (t1).
On Eviews, I did the following:
Quick> Equation Estimation > Specification> I chose ARCH under Estimation Settings >For ARCHM I pulled down and chose Variance > Now I'm stuck what to put under "Mean Equation" and "Variance Regressors".
Should I also be checking the box on "Options" for "Coefficient Covariance" ?
Since this is a completely "conditional" model, I am a bit lost with the specifications....Again, being a novice with Eviews and Econometrics, any help will be greatly appreciated.
Thanks again,
Best Regards,
Srana
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Equation Estimation dialog box allows you to model univariate GARCH models. You seem to look for bivariate GARCH estimation. System Estimation offers several multivariate GARCH methods, but you cannot specify "inmean" models there. Unfortunately, you'll have to build your own model within the maximum likelihood framework.
Since this can be a very complicated task for "a novice in EViews and Econometrics", I suggest you to refer to a textbook for the theoretical background and go over EViews' help files and the forum for similar examples.
Since this can be a very complicated task for "a novice in EViews and Econometrics", I suggest you to refer to a textbook for the theoretical background and go over EViews' help files and the forum for similar examples.
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Thanks for your reply Trubador, will do accordingly.

 Posts: 39
 Joined: Thu Apr 24, 2014 3:51 am
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Hi, can someone provide the extended codes of a DCCGARCH model for a 5 variable model?
Thank you
Thank you

 Posts: 4
 Joined: Sat Feb 28, 2015 2:15 am
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Hi all,
Currently I am trying to run a multivariate GARCH model spproach. But I still do not know the method run Multivariate models. Hope to get help from you. The model is attached below.I looking forward to find methods to run this model. You can guide your steps to run detail, because I used Eview not proficient. Thank' you very much.
Currently I am trying to run a multivariate GARCH model spproach. But I still do not know the method run Multivariate models. Hope to get help from you. The model is attached below.I looking forward to find methods to run this model. You can guide your steps to run detail, because I used Eview not proficient. Thank' you very much.
 Attachments

 Document 2.doc
 Model
 (129.5 KiB) Downloaded 130 times

 Book1.xls
 DATA
 (993.5 KiB) Downloaded 126 times
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Dear all,
I tried four variate Garch. But i am unable to get the output due to some errors. Pls let me know where i am wrong.
' dependent variables of all series must be continues
series y1 = dlog(us_dollar)
series y2 = dlog(euro)
series y3 = dlog(pound_sterling)
series y4 = dlog(japanese_yen)
' set sample
' first observation of s1 need to be one or two periods after
' the first observation of s0
sample s0 1/4/1999 6/12/2015
sample s1 1/6/1999 6/12/2015
' initialization of parameters and starting values
' change below only to change the specification of model
smpl s0
'get starting values from univariate GARCH
equation eq1.arch(m=100,c=1e5) y1 c
equation eq2.arch(m=100,c=1e5) y2 c
equation eq3.arch(m=100,c=1e5) y3 c
equation eq4.arch(m=100,c=1e5) y4 c
' declare coef vectors to use in GARCH model
coef(4) lambda
lambda (1) = eq1.c(1)
lambda (2) = eq2.c(1)
lambda (3) = eq3.c(1)
lambda (4) = eq4.c(1)
coef(4) mu
mu(1) = eq1.c(1)
mu(2) = eq2.c(1)
mu(3) = eq3.c(1)
mu(4) = eq4.c(1)
coef(10) omega
omega(1) = (eq1.c(2))^.5
omega(2) = 0
omega(3) = 0
omega(4) = 0
omega(5) = eq2.c(2)^.5
omega(6) = 0
omega(7) = 0
omega(8) = eq3.c(2)^.5
omega(9) = 0
omega(10) = eq4.c(2)^.5
coef(4) alpha
alpha(1) = (eq1.c(3))^.5
alpha(2) = (eq2.c(3))^.5
alpha(3) = (eq3.c(3))^.5
alpha(4) = (eq4.c(3))^.5
coef(4) beta
beta(1) = (eq1.c(4))^.5
beta(2) = (eq2.c(4))^.5
beta(3) = (eq3.c(4))^.5
beta(4) = (eq4.c(4))^.5
' use sample varcov as starting value of variancecovariance matrix
series cov_y1y2 = @cov(y1mu(1), y2mu(2))
series cov_y1y3 = @cov(y1mu(1), y3mu(3))
series cov_y1y4 = @cov(y1mu(1), y4mu(4))
series cov_y2y3 = @cov(y2mu(2), y3mu(3))
series cov_y2y4 = @cov(y2mu(2), y4mu(4))
series cov_y3y4 = @cov(y3mu(3), y4mu(4))
series var_y1 = @var(y1)
series var_y2 = @var(y2)
series var_y3 = @var(y3)
series var_y4 = @var(y4)
series sqres1 = (y1mu(1))^2
series sqres2 = (y2mu(2))^2
series sqres3 = (y3mu(3))^2
series sqres4 = (y4mu(4))^2
series res1res2 = (y1mu(1))*(y2mu(2))
series res1res3 = (y1mu(1))*(y3mu(3))
series res1res4 = (y1mu(1))*(y4mu(4))
series res2res3 = (y2mu(2))*(y3mu(3))
series res2res4 = (y2mu(2))*(y4mu(4))
series res3res4 = (y3mu(3))*(y4mu(4))
' constant adjustment for log likelihood
!mlog2pi = 4*log(2*@acos(1))
' ...........................................................
' LOG LIKELIHOOD
' set up the likelihood
' 1) open a new blank likelihood object name tvgarch
' 2) specify the log likelihood model by append
' ...........................................................
logl fvgarch
' squared errors and cross errors
fvgarch.append @logl logl
fvgarch.append sqres1 = (y1mu(1))^2
fvgarch.append sqres2 = (y2mu(2))^2
fvgarch.append sqres3 = (y3mu(3))^2
fvgarch.append sqres4 = (y4mu(4))^2
fvgarch.append res1res2 = (y1mu(1))*(y2mu(2))
fvgarch.append res1res3 = (y1mu(1))*(y3mu(3))
fvgarch.append res1res4 = (y1mu(1))*(y4mu(4))
fvgarch.append res2res3 = (y2mu(2))*(y3mu(3))
fvgarch.append res2res4 = (y2mu(2))*(y4mu(4))
fvgarch.append res3res4 = (y3mu(3))*(y4mu(4))
' variance and covariance series
fvgarch.append var_y1 = omega(1)^2 + beta(1)^2*var_y1(1) + alpha(1)^2*sqres1(1)
fvgarch.append var_y2 = omega(2)^2+omega(5)^2 + beta(2)^2*var_y2(1) + alpha(2)^2*sqres2(1)
fvgarch.append var_y3 = omega(3)^2+omega(6)^2+omega(8)^2 + beta(3)^2*var_y3(1) + alpha(3)^2*sqres3(1)
fvgarch.append var_y4 = omega(4)^2+omega(7)^2+omega(9)^2 +omega(10)^2+ beta(4)^2*var_y4(1) + alpha(4)^2*sqres4(1)
fvgarch.append cov_y1y2 = omega(1)*omega(2)* + beta(2)*beta(1)*cov_y1y2(1) + alpha(2)*alpha(1)*res1res2(1)
fvgarch.append cov_y1y3 = omega(1)*omega(3) + beta(3)*beta(1)*cov_y1y3(1) + alpha(3)*alpha(1)*res1res3(1)
fvgarch.append cov_y1y4 = omega(1)*omega(4) + beta(4)*beta(1)*cov_y1y4(1) + alpha(4)*alpha(1)*res1res4(1)
fvgarch.append cov_y2y3 = omega(2)*omega(3) + omega(4)*omega(5) + beta(3)*beta(2)*cov_y2y3(1) + alpha(3)*alpha(2)*res2res3(1)
fvgarch.append cov_y2y4 = omega(2)*omega(4) + omega(5)*omega(6) + beta(4)*beta(2)*cov_y2y4(1) + alpha(4)*alpha(2)*res2res4(1)
fvgarch.append cov_y3y4 = omega(3)*omega(5) + omega(6)*omega(7) + beta(3)*beta(4)*cov_y3y4(1) + alpha(3)*alpha(4)*res3res4(1)
' determinant of the variancecovariance matrix
fvgarch.append deth = var_y1*var_y2*var_y3*var_y4var_y1*cov_y3y4*cov_y2y3^2cov_y1y2^2*var_y4+2*cov_y2y3*cov_y3y4*cov_y1y4cov_y1y4^2*
var_y3+2*cov_y1y2*cov_y2y3*cov_y1y3*cov_y2y4cov_y2y4^2*cov_y2y3*cov_y1y2*cov_y1y4*cov_y3y4cov_y3y4^2*var_y2
' calculate the elements of the inverse of var_cov (H) matrix
' numbered as vech(inv(H))
fvgarch.append invh1 = (var_y3*var_y4cov_y3y4^2)/deth
fvgarch.append invh2 = (cov_y1y2*var_y4 cov_y1y3*cov_y1y4* cov_y2y3* cov_y2y4*cov_y3y4*)/deth
fvgarch.append invh3 = (cov_y1y2*cov_y2y3*cov_y1y3* cov_y2y4*cov_y3y4cov_y1y4*var_y3)/deth
fvgarch.append invh4 = (var_y1*var_y4cov_y1y4^2)/deth
fvgarch.append invh5 = (var_y1*cov_y2y3cov_y1y2*cov_y1y3 * cov_y2y4*cov_y3y4*cov_y1y4)/deth
fvgarch.append invh6= (var_y2*var_y4cov_y2y4^2)/deth
fvgarch.append invh7 = (cov_y1y2*cov_y2y3*cov_y1y4* cov_y2y4*cov_y3y4cov_y1y3*var_y2)/deth
fvgarch.append invh8= (var_y1*var_y2cov_y1y2^2)/deth
fvgarch.append invh9= (var_y1*var_y3cov_y1y3^2)/deth
fvgarch.append invh10 = (var_y2*var_y3cov_y2y3^2)/deth
fvgarch.append invh11 = (cov_y1y2*cov_y2y3*cov_y1y3* cov_y1y4*cov_y3y4cov_y2y4*var_y3)/deth
fvgarch.append invh12 = (var_y1*cov_y3y4cov_y1y2*cov_y1y3 * cov_y2y3*cov_y2y4*cov_y1y4)/deth
' loglikelihood series
tvgarch.append logl = 0.5*(!mlog2pi + (invh1*sqres1+invh4*sqres2+invh6*sqres3 +2*invh2*res1res2 +2*invh3*res1res3+2*invh5*res2res3 ) + log(deth))
tvgarch.append logl = 0.5*(!mlog2pi + (invh1*sqres1+invh4*sqres2+invh6*sqres3 ++invh8*sqres4 +invh9*sqres5 + invh10*sqres6 +2*invh2*res1res2 +2*invh3*res1res4+2*invh5*res2res3) + 2*invh7*res1res3)+2*invh11*res2res4)+ 2*invh12*res1res3)+log(deth))
' remove some of the intermediary series
'tvgarch.append @temp invh1 invh2 invh3 invh4 invh5 invh6 sqres1 sqres2 sqres3 res1res2 res1res3 res2res3 deth
' estimate the model
smpl s1
tvgarch.ml(showopts, m=100, c=1e5)
' change below to display different output
show tvgarch.output
graph var.line var_y1 var_y2 var_y3 var_y4
graph cov.line cov_y1y2 cov_y1y3 cov_y2y3 cov_y1y4 cov_y2y4 cov_y3y4
show var
show cov
' LR statistic for univariate vs trivariate
scalar lr = 2*(eq1.@logl + eq2.@logl + eq3.@logl  tvgarch.@logl)
scalar lr_pval = 1  @cchisq(lr,3)
I tried four variate Garch. But i am unable to get the output due to some errors. Pls let me know where i am wrong.
' dependent variables of all series must be continues
series y1 = dlog(us_dollar)
series y2 = dlog(euro)
series y3 = dlog(pound_sterling)
series y4 = dlog(japanese_yen)
' set sample
' first observation of s1 need to be one or two periods after
' the first observation of s0
sample s0 1/4/1999 6/12/2015
sample s1 1/6/1999 6/12/2015
' initialization of parameters and starting values
' change below only to change the specification of model
smpl s0
'get starting values from univariate GARCH
equation eq1.arch(m=100,c=1e5) y1 c
equation eq2.arch(m=100,c=1e5) y2 c
equation eq3.arch(m=100,c=1e5) y3 c
equation eq4.arch(m=100,c=1e5) y4 c
' declare coef vectors to use in GARCH model
coef(4) lambda
lambda (1) = eq1.c(1)
lambda (2) = eq2.c(1)
lambda (3) = eq3.c(1)
lambda (4) = eq4.c(1)
coef(4) mu
mu(1) = eq1.c(1)
mu(2) = eq2.c(1)
mu(3) = eq3.c(1)
mu(4) = eq4.c(1)
coef(10) omega
omega(1) = (eq1.c(2))^.5
omega(2) = 0
omega(3) = 0
omega(4) = 0
omega(5) = eq2.c(2)^.5
omega(6) = 0
omega(7) = 0
omega(8) = eq3.c(2)^.5
omega(9) = 0
omega(10) = eq4.c(2)^.5
coef(4) alpha
alpha(1) = (eq1.c(3))^.5
alpha(2) = (eq2.c(3))^.5
alpha(3) = (eq3.c(3))^.5
alpha(4) = (eq4.c(3))^.5
coef(4) beta
beta(1) = (eq1.c(4))^.5
beta(2) = (eq2.c(4))^.5
beta(3) = (eq3.c(4))^.5
beta(4) = (eq4.c(4))^.5
' use sample varcov as starting value of variancecovariance matrix
series cov_y1y2 = @cov(y1mu(1), y2mu(2))
series cov_y1y3 = @cov(y1mu(1), y3mu(3))
series cov_y1y4 = @cov(y1mu(1), y4mu(4))
series cov_y2y3 = @cov(y2mu(2), y3mu(3))
series cov_y2y4 = @cov(y2mu(2), y4mu(4))
series cov_y3y4 = @cov(y3mu(3), y4mu(4))
series var_y1 = @var(y1)
series var_y2 = @var(y2)
series var_y3 = @var(y3)
series var_y4 = @var(y4)
series sqres1 = (y1mu(1))^2
series sqres2 = (y2mu(2))^2
series sqres3 = (y3mu(3))^2
series sqres4 = (y4mu(4))^2
series res1res2 = (y1mu(1))*(y2mu(2))
series res1res3 = (y1mu(1))*(y3mu(3))
series res1res4 = (y1mu(1))*(y4mu(4))
series res2res3 = (y2mu(2))*(y3mu(3))
series res2res4 = (y2mu(2))*(y4mu(4))
series res3res4 = (y3mu(3))*(y4mu(4))
' constant adjustment for log likelihood
!mlog2pi = 4*log(2*@acos(1))
' ...........................................................
' LOG LIKELIHOOD
' set up the likelihood
' 1) open a new blank likelihood object name tvgarch
' 2) specify the log likelihood model by append
' ...........................................................
logl fvgarch
' squared errors and cross errors
fvgarch.append @logl logl
fvgarch.append sqres1 = (y1mu(1))^2
fvgarch.append sqres2 = (y2mu(2))^2
fvgarch.append sqres3 = (y3mu(3))^2
fvgarch.append sqres4 = (y4mu(4))^2
fvgarch.append res1res2 = (y1mu(1))*(y2mu(2))
fvgarch.append res1res3 = (y1mu(1))*(y3mu(3))
fvgarch.append res1res4 = (y1mu(1))*(y4mu(4))
fvgarch.append res2res3 = (y2mu(2))*(y3mu(3))
fvgarch.append res2res4 = (y2mu(2))*(y4mu(4))
fvgarch.append res3res4 = (y3mu(3))*(y4mu(4))
' variance and covariance series
fvgarch.append var_y1 = omega(1)^2 + beta(1)^2*var_y1(1) + alpha(1)^2*sqres1(1)
fvgarch.append var_y2 = omega(2)^2+omega(5)^2 + beta(2)^2*var_y2(1) + alpha(2)^2*sqres2(1)
fvgarch.append var_y3 = omega(3)^2+omega(6)^2+omega(8)^2 + beta(3)^2*var_y3(1) + alpha(3)^2*sqres3(1)
fvgarch.append var_y4 = omega(4)^2+omega(7)^2+omega(9)^2 +omega(10)^2+ beta(4)^2*var_y4(1) + alpha(4)^2*sqres4(1)
fvgarch.append cov_y1y2 = omega(1)*omega(2)* + beta(2)*beta(1)*cov_y1y2(1) + alpha(2)*alpha(1)*res1res2(1)
fvgarch.append cov_y1y3 = omega(1)*omega(3) + beta(3)*beta(1)*cov_y1y3(1) + alpha(3)*alpha(1)*res1res3(1)
fvgarch.append cov_y1y4 = omega(1)*omega(4) + beta(4)*beta(1)*cov_y1y4(1) + alpha(4)*alpha(1)*res1res4(1)
fvgarch.append cov_y2y3 = omega(2)*omega(3) + omega(4)*omega(5) + beta(3)*beta(2)*cov_y2y3(1) + alpha(3)*alpha(2)*res2res3(1)
fvgarch.append cov_y2y4 = omega(2)*omega(4) + omega(5)*omega(6) + beta(4)*beta(2)*cov_y2y4(1) + alpha(4)*alpha(2)*res2res4(1)
fvgarch.append cov_y3y4 = omega(3)*omega(5) + omega(6)*omega(7) + beta(3)*beta(4)*cov_y3y4(1) + alpha(3)*alpha(4)*res3res4(1)
' determinant of the variancecovariance matrix
fvgarch.append deth = var_y1*var_y2*var_y3*var_y4var_y1*cov_y3y4*cov_y2y3^2cov_y1y2^2*var_y4+2*cov_y2y3*cov_y3y4*cov_y1y4cov_y1y4^2*
var_y3+2*cov_y1y2*cov_y2y3*cov_y1y3*cov_y2y4cov_y2y4^2*cov_y2y3*cov_y1y2*cov_y1y4*cov_y3y4cov_y3y4^2*var_y2
' calculate the elements of the inverse of var_cov (H) matrix
' numbered as vech(inv(H))
fvgarch.append invh1 = (var_y3*var_y4cov_y3y4^2)/deth
fvgarch.append invh2 = (cov_y1y2*var_y4 cov_y1y3*cov_y1y4* cov_y2y3* cov_y2y4*cov_y3y4*)/deth
fvgarch.append invh3 = (cov_y1y2*cov_y2y3*cov_y1y3* cov_y2y4*cov_y3y4cov_y1y4*var_y3)/deth
fvgarch.append invh4 = (var_y1*var_y4cov_y1y4^2)/deth
fvgarch.append invh5 = (var_y1*cov_y2y3cov_y1y2*cov_y1y3 * cov_y2y4*cov_y3y4*cov_y1y4)/deth
fvgarch.append invh6= (var_y2*var_y4cov_y2y4^2)/deth
fvgarch.append invh7 = (cov_y1y2*cov_y2y3*cov_y1y4* cov_y2y4*cov_y3y4cov_y1y3*var_y2)/deth
fvgarch.append invh8= (var_y1*var_y2cov_y1y2^2)/deth
fvgarch.append invh9= (var_y1*var_y3cov_y1y3^2)/deth
fvgarch.append invh10 = (var_y2*var_y3cov_y2y3^2)/deth
fvgarch.append invh11 = (cov_y1y2*cov_y2y3*cov_y1y3* cov_y1y4*cov_y3y4cov_y2y4*var_y3)/deth
fvgarch.append invh12 = (var_y1*cov_y3y4cov_y1y2*cov_y1y3 * cov_y2y3*cov_y2y4*cov_y1y4)/deth
' loglikelihood series
tvgarch.append logl = 0.5*(!mlog2pi + (invh1*sqres1+invh4*sqres2+invh6*sqres3 +2*invh2*res1res2 +2*invh3*res1res3+2*invh5*res2res3 ) + log(deth))
tvgarch.append logl = 0.5*(!mlog2pi + (invh1*sqres1+invh4*sqres2+invh6*sqres3 ++invh8*sqres4 +invh9*sqres5 + invh10*sqres6 +2*invh2*res1res2 +2*invh3*res1res4+2*invh5*res2res3) + 2*invh7*res1res3)+2*invh11*res2res4)+ 2*invh12*res1res3)+log(deth))
' remove some of the intermediary series
'tvgarch.append @temp invh1 invh2 invh3 invh4 invh5 invh6 sqres1 sqres2 sqres3 res1res2 res1res3 res2res3 deth
' estimate the model
smpl s1
tvgarch.ml(showopts, m=100, c=1e5)
' change below to display different output
show tvgarch.output
graph var.line var_y1 var_y2 var_y3 var_y4
graph cov.line cov_y1y2 cov_y1y3 cov_y2y3 cov_y1y4 cov_y2y4 cov_y3y4
show var
show cov
' LR statistic for univariate vs trivariate
scalar lr = 2*(eq1.@logl + eq2.@logl + eq3.@logl  tvgarch.@logl)
scalar lr_pval = 1  @cchisq(lr,3)
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Jayaraj wrote:I tried four variate Garch. But i am unable to get the output due to some errors. Pls let me know where i am wrong.
There is no way to know what is wrong without having the actual data/workfile. Besides, I am fairly sure that the answer is already out there in the forum. Please search previous posts or similar threads.
Re: HOW TO ESTIMATE A MULTIVARIATE GARCHM MODEL?
Hi,
I am trying to estimate a bivariategarchinmean for the Mexican economy and having a failure code "Missing values in @LOGL series at current coefficients at observation 2000M02 in "DO_ BV_GARCH_M.ML (SHOWOPTS, M=100, C=1E5)". Although I change the s1, I am still having the same failure. I followed the whole previous posts but could not solve the problem. Can anyone help me what is the problem or how to solve it? The files are attached.
I am trying to estimate a bivariategarchinmean for the Mexican economy and having a failure code "Missing values in @LOGL series at current coefficients at observation 2000M02 in "DO_ BV_GARCH_M.ML (SHOWOPTS, M=100, C=1E5)". Although I change the s1, I am still having the same failure. I followed the whole previous posts but could not solve the problem. Can anyone help me what is the problem or how to solve it? The files are attached.
 Attachments

 bv_garch_m.prg
 (3.71 KiB) Downloaded 32 times

 bv_data.wf1
 (18.44 KiB) Downloaded 28 times
Who is online
Users browsing this forum: No registered users and 7 guests