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Hello,

A recent patch unfortunately introduced a bug into wildcard handling. The next EViews patch will correct the issue.

The next patch will fix this issue.

Hello,

Ordered models have an extra degree of freedom I believe, so this may be an instance of the dummy variable trap. Try @expand(var01, @droplast) instead.

Hello, Just to clarify, in EViews' terminology recursive blocks are those in which the endogenous variables are related only via unidirectional dependencies, e.g. X = f(Y) and Y = f(Z). In contrast, simultaneous blocks include bidirectional dependencies (direct or indirect), e.g., X = f(Y) and Y = f...

Hello,

That error usually indicates a syntactic problem with the model specification, e.g., a syntax error in one of the model's equations. You should be able to locate the problem equation(s) by examining the model's equation view, where such errors will be highlighted in red.

Hello,

I can confirm the problem. The most recent patch appears to have introduced this bug. We'll work on a fix ASAP.

Hello, I'm afraid there isn't a way to undefine a variable such that it won't trigger substitution later in an EViews program. However, since you're running multiple programs sequentially, you can isolate the individual programs' variables from one another by using "exec" instead of "...

Hello, Just a little extra information about include statements that might help in the future, such statements are functionally equivalent to copying the EViews code from the included file into the including file at the inclusion point. Consequently, the program will be ill-formed if the included fi...

Hello,

Are you trying to solve the model over the entire period (2000-2024) or just the forecast period (2021-2024)?

Hello,

I believe you're correct.

Hello, Regarding (1), you're follow-up is correct. The shocks are structural shocks and by construction the structural residuals have an identity covariance matrix. A one unit structural shock and one S.D. structural shock are therefore identical, and can be interpreted directly as the columns of th...

Hello, Just for the sake of a complete example here in my response, let me address your questions using a trivial artificial VAR. Regarding (1), matrix/vector you must specify holds the shocks in its column(s), with the rows representing the different variables. For the basic Cholesky decomposition,...

Hello, Regarding (1) and (2), here's a quick program that verifies that the S and F matrices behave as expected for a trivial artificial SVAR. If you run the last four statements for your own SVAR, do the results not match the mentioned expectations? create u 100 series x = rnd series y = 2 * x + nr...

Quite right. Either of the first two SVAR restriction presets will produce an S matrix equivalent to a Cholesky decomposition.

Hello,

After you perform either type of structural analysis, you can use the VAR object's @impfact data member to retrieve the underlying factorization matrix.