# Tag Info

2

Aside the solutions mentioned in the other answer you can also estimate it in: Julia Matlab Octave (this is just free version of matlab with some small changes)

2

EViews can do SVARs with custom restrictions, including some presets. You may check out their online documentation. Btw, this one is the only one to my knowledge that also does long-term restrictions on the residuals. Pretty sure that JMulti can also do the kind of restrictions you are after. You can get it here for free. It's quite old so I am not sure ...

0

How you call your shocks should not be based alone on which variable they hit, but also how, exactly, they hit it. What matters here is the structure of the impact matrix, such as in your example 2. I assume it shows the long-run impact (although I cannot be sure). On a higher levels, how to interpret the shocks also depends on the angle of analysis. If it ...

4

My short answer is that if shocks are zero over the whole horizon (history, present, and future) then the variables in a VAR will not move at all, just as in a DSGE. You cannot assume a starting point off-equilibrium without implicitly assuming that at least one historical shock was different from zero. If only all future shocks are zero, then the impact of ...

2

You don't provide much information. Here is what I see here. The theoretical background for the first case is the AS-AD model. The exact specification seems to be an A-type VAR. A positive aggregate demand shock has a positive effect on both $\Delta y$ ($\Rightarrow a_{11} >0$) and $\pi$ ($\Rightarrow a_{21} >0$). A positive supply shock has a positive ...

3

Zero shocks do not imply zero variations in a VAR model. In a stationary VAR model, if the shocks starting from a time period $t=\tau$ are all permanently zero, the variables will converge to their unconditional means, but this will not happen immediately. Due to the autoregressive structure, the convergence will be gradual and will never cease to happen, as ...

8

If I include $z_1$ in the model, like this: $$> y = \beta_0 + \beta_1 x + \beta_2 z_1 + e, >$$ Does that mean that $\beta_1$ is predominantly capturing the effect of $z_2$? Yes. This can be seen using the Frish-Waugh-Lovell theorem: If you regress: $$y = \beta_0 + \beta_1 x + \beta_2 z_1 + e,$$ then $\beta_1$ will be the same as the corresponding ...

1

I'm not trying to be picky, but just in case, there is no such thing as the consistent but inefficient estimator. Perhaps you mean a consistent but inefficient estimator? Your estimator is the OLS estimator. See $X'X$ cancelled. If you only use the moment conditions $E(x_t u_t)=0$, then OLS is the only GMM (or actually MM) estimator that can follow. As the ...

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