Time-varying beta model
Posted: Tue Sep 11, 2012 10:05 pm
Dear EViewers,
I am trying to run a time-varying beta regression model, which looks like this:
ri,t= α + bt rm,t+ et
bt= c0+ c1*D1 + c2*D2 + c3*D3
ht= π+ αet-1^2+ βht-1
The first equation specifies the general relationship, while the second equation specifies the beta as a dynamic process (which depends on three dummy variables, D1, D2 & D3).
I have time series data for: ri,t, rm,t, D1, D2, D3.
I need to obtain the coefficients: bt, c0, c1, c2 & c3.
The third equation specifies a GARCH(1,1) model. I was told that this system of equations should be estimated simultaneously using maximum likelihood.
I cannot figure out the proper way of estimating this model in EViews ... do I specify a System, or use a state space object, or ...? The most important for me is estimating at least the first two equations together.
Please help! I am completely lost on this!!!
I am trying to run a time-varying beta regression model, which looks like this:
ri,t= α + bt rm,t+ et
bt= c0+ c1*D1 + c2*D2 + c3*D3
ht= π+ αet-1^2+ βht-1
The first equation specifies the general relationship, while the second equation specifies the beta as a dynamic process (which depends on three dummy variables, D1, D2 & D3).
I have time series data for: ri,t, rm,t, D1, D2, D3.
I need to obtain the coefficients: bt, c0, c1, c2 & c3.
The third equation specifies a GARCH(1,1) model. I was told that this system of equations should be estimated simultaneously using maximum likelihood.
I cannot figure out the proper way of estimating this model in EViews ... do I specify a System, or use a state space object, or ...? The most important for me is estimating at least the first two equations together.
Please help! I am completely lost on this!!!