I have been largely benefited from this forum every time I post a question or I am looking for a question someone had previously asked.
For today, my model is a TVP-PVAR in a normal linear state space model composed of the State Equation and the Measurement Equation. . There is a large amount of data, therefore I'm using numerous variables in the model. My model equations are the ones given below and for a panel after linearization panel where according to Ciccareli and Canova (2013) can be estimated as such. My model of equations are therefore defined .

Where X ̃t=XtΞ and ut=Xt′+ut with Ut∼N(0,(I+σ2Xt′Xt))×Σ
Then this can be estimated a normal var or a bayesian var where the time variation is given by the unit root. Whether then the estimation is made by classic tolls or Bayesian vars is indifferent as long a the likelihood is well defined
I know that in Stata 17 there are some tools for estimating bayesian vars but could anyone of here be of help on how to procedure, eventually with a code. I
can provide data as long as I can find a good solution
I would immensely greatfull for any help
For today, my model is a TVP-PVAR in a normal linear state space model composed of the State Equation and the Measurement Equation. . There is a large amount of data, therefore I'm using numerous variables in the model. My model equations are the ones given below and for a panel after linearization panel where according to Ciccareli and Canova (2013) can be estimated as such. My model of equations are therefore defined .
Where X ̃t=XtΞ and ut=Xt′+ut with Ut∼N(0,(I+σ2Xt′Xt))×Σ
Then this can be estimated a normal var or a bayesian var where the time variation is given by the unit root. Whether then the estimation is made by classic tolls or Bayesian vars is indifferent as long a the likelihood is well defined
I know that in Stata 17 there are some tools for estimating bayesian vars but could anyone of here be of help on how to procedure, eventually with a code. I
can provide data as long as I can find a good solution
I would immensely greatfull for any help
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