<p>I came across <a href="https://www.eviews.com/StructVAR/structvar.pdf" rel="nofollow noreferrer">this</a> text that seems to estimate a non-recursive SVAR in Eviews, but the restrictions are kinda different from what I expected.</p>
<p>In their recursive SVAR, they have the following results in Eviews which are the restrictions I expected given the model.</p>
<p><a href="https://i.sstatic.net/nnilW.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/nnilW.png" alt="enter image description here" /></a></p>
<p>The results above has the following restrictions for matrix A and B. So we know the coefficients of the contemporaneous variables.</p>
<p><a href="https://i.sstatic.net/MUidU.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MUidU.png" alt="enter image description here" /></a></p>
<p>In the <strong>non-recursive</strong> SVAR, they have the following results</p>
<p><a href="https://i.sstatic.net/NoXix.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/NoXix.png" alt="enter image description here" /></a></p>
<p>But I expected the following retrictions for matrices A and B instead.</p>
<p><a href="https://i.sstatic.net/YqUIo.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/YqUIo.png" alt="enter image description here" /></a></p>
<p>As we can see, their results show the restrictions below. How can we then know the coefficients of the contemporaneous variables? I know e = (A^-1)Bu will match the model in the non-recursive example above, but I don't understand why A is an identity matrix and B is not diagonal. And why my expectation in the red matrixes above is wrong. I actually want B to be diagonal or identity matrix in the non-recursive SVAR, not matrix A. Thanks for any help.</p>
<p><a href="https://i.sstatic.net/fW8Fo.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/fW8Fo.png" alt="enter image description here" /></a></p>

CC BY-SA 4.0
2021-07-10T08:21:59.087Z
Does your confusion have something to do with the fact that the ordering of the coefficients in EViews is columnwise in the A matrix and indices with zero are skipped? for example, the columns of a lower-triangular matrix would be (1) c1, c2, c3; (2) 0, c4, c5; (3) c6.
BrsG
CC BY-SA 4.0
2021-07-11T10:59:15.300Z
@BrsG My confusion is that if A matrix is diagonal, then what are the contemporaneous coefficients? When I read books on SVAR, matrix A (containing the contemporaneous restrictions) is never diagonal, yeah? Like in Ae = Bu, where u orthogonal structural shock, how can A be diagonal? I guess, then there are no contemporaneous effects in the model? Thanks!
Emmanuel Ameyaw
CC BY-SA 4.0
2021-07-11T15:29:39.470Z
Indeed, when A is diagonal, this corresponds to the special case with no contemporaneous interaction between the variables. In $Ae=Bu$ case, you can impose restrictions on $A$, $B$, or $S=A^{-1}B$, each meaning different things.
BrsG
CC BY-SA 4.0
2021-07-18T06:09:15.897Z
@BrsG. Thanks for the reply. Lemme ask though for a hint. When you impose restrictions directly on S, what is your goal? Only correct identification of the shocks?
Emmanuel Ameyaw
CC BY-SA 4.0
2021-07-19T08:32:17.893Z
$S$ gives you the long-term restrictions. For example, by setting a zero in the appropriate position you can impose that shocks have no impact on a certain variable at an infinite horizon.
BrsG
<recursive-macroeconomics><svar>
How to estimate a non-resursive SVAR in Eviews/Stata?
Emmanuel Ameyaw
# How to estimate a non-resursive SVAR in Eviews/Stata?

(Asked by: username_0 on 2021-06-26)

I came across [this](https://www.eviews.com/StructVAR/structvar.pdf) text that seems to estimate a non-recursive SVAR in Eviews, but the restrictions are kinda different from what I expected.

In their recursive SVAR, they have the following results in Eviews which are the restrictions I expected given the model.

[![enter image description here](https://i.sstatic.net/nnilW.png)](https://i.sstatic.net/nnilW.png)

The results above has the following restrictions for matrix A and B. So we know the coefficients of the contemporaneous variables.

[![enter image description here](https://i.sstatic.net/MUidU.png)](https://i.sstatic.net/MUidU.png)

In the **non-recursive** SVAR, they have the following results

[![enter image description here](https://i.sstatic.net/NoXix.png)](https://i.sstatic.net/NoXix.png)

But I expected the following retrictions for matrices A and B instead.

[![enter image description here](https://i.sstatic.net/YqUIo.png)](https://i.sstatic.net/YqUIo.png)

As we can see, their results show the restrictions below. How can we then know the coefficients of the contemporaneous variables? I know e = (A^-1)Bu will match the model in the non-recursive example above, but I don't understand why A is an identity matrix and B is not diagonal. And why my expectation in the red matrixes above is wrong. I actually want B to be diagonal or identity matrix in the non-recursive SVAR, not matrix A. Thanks for any help.

[![enter image description here](https://i.sstatic.net/fW8Fo.png)](https://i.sstatic.net/fW8Fo.png)

**Tags:** recursive-macroeconomics, svar
> **Comments:**
> **username_1**: Does your confusion have something to do with the fact that the ordering of the coefficients in EViews is columnwise in the A matrix and indices with zero are skipped? for example, the columns of a lower-triangular matrix would be (1) c1, c2, c3; (2) 0, c4, c5; (3) c6.  
> **username_0**: @BrsG My confusion is that if A matrix is diagonal, then what are the contemporaneous coefficients? When I read books on SVAR, matrix A (containing the contemporaneous restrictions) is never diagonal, yeah? Like in Ae = Bu, where u orthogonal structural shock, how can A be diagonal? I guess, then there are no contemporaneous effects in the model? Thanks!  
> **username_1**: Indeed, when A is diagonal, this corresponds to the special case with no contemporaneous interaction between the variables. In $Ae=Bu$ case, you can impose restrictions on $A$, $B$, or $S=A^{-1}B$, each meaning different things.  
> **username_0**: @BrsG. Thanks for the reply. Lemme ask though for a hint. When you impose restrictions directly on S, what is your goal? Only correct identification of the shocks?  
> **username_1**: $S$ gives you the long-term restrictions. For example, by setting a zero in the appropriate position you can impose that shocks have no impact on a certain variable at an infinite horizon.  

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## 0 answers

