Dynamic factor model (SS)
Posted: Sun May 08, 2011 4:03 pm
Hi,
I was trying to estimate a dynamic factor model in EViews, but I seem to get stuck every time.
Here's the idea: to keep things simple, I took inflation data from Germany, France and Denmark. The model I would like to estimate is:
Inflation (i,t) = B(i) * F(t) + e(i,t)
F (t) = c1 + c2 * F(t-1) + c3 * F(t-2) + u(t)
with F(t) being the dynamic factor.
I thought that the following state-space model would do the trick:
@signal france = c(11)*sv1 + [var = exp(c(1))]
@signal germany= c(12)*sv1 + [var=exp(c(2))]
@signal denmark = c(13)*sv1 + [var=exp(c(3))]
@state sv1 = c(21) + c(22)*sv1(-1) + c(23)*sv2 + [var=exp(c(24))]
@state sv2 = sv1(-1)
However, the error I get is:
"Invalid lags or leads in state variables in state equation '@state sv1 = c(21) + c(22)*sv1(-1) + c(23)*sv2 + [var=exp(c(24))]'".
I've double-checked the manual, and I don't see why the lagged sv1 in the state equation should give me an error.... I'd be grateful if anyone could point me into the right direction.
Many thanks, Philipp
I was trying to estimate a dynamic factor model in EViews, but I seem to get stuck every time.
Here's the idea: to keep things simple, I took inflation data from Germany, France and Denmark. The model I would like to estimate is:
Inflation (i,t) = B(i) * F(t) + e(i,t)
F (t) = c1 + c2 * F(t-1) + c3 * F(t-2) + u(t)
with F(t) being the dynamic factor.
I thought that the following state-space model would do the trick:
@signal france = c(11)*sv1 + [var = exp(c(1))]
@signal germany= c(12)*sv1 + [var=exp(c(2))]
@signal denmark = c(13)*sv1 + [var=exp(c(3))]
@state sv1 = c(21) + c(22)*sv1(-1) + c(23)*sv2 + [var=exp(c(24))]
@state sv2 = sv1(-1)
However, the error I get is:
"Invalid lags or leads in state variables in state equation '@state sv1 = c(21) + c(22)*sv1(-1) + c(23)*sv2 + [var=exp(c(24))]'".
I've double-checked the manual, and I don't see why the lagged sv1 in the state equation should give me an error.... I'd be grateful if anyone could point me into the right direction.
Many thanks, Philipp