[Scilab-users] Using GROCER ms_var parameters for forecasting

[Eric Dubois]

Dear Brian.

If I have well understood, you want:
- to estimate a ms_var model on a subset of your dataset;
- recover the estimated parameters;
- and calculate the filtered state probabilities on the other part of your
dataset with these parameters.

This can be done:
- the function MSVAR_Filt calculates among other the filetered
probabilities (5th output);
- the function needs among other things the parameters of the model; they
can be recovered from the output tlist of function ms_var; if give it the
name res (with --> res=ms_var(...)): this is the field 'coeff' in the
output tlist (res('coeff') with this example);

But the function MSVAR_Filt also has to be fed with matrices y_hat, x_hat
and z_hat that are matrices derived from the matrix of endogenous and
exogenous variables (see function ms_var to see how it is done).

If you are not too in a hurry, I can write the function that gathers all
these operations within a few weeks.

Éric.

2015-02-12 16:56 GMT+01:00 Brian Bouterse <bmbouter at gmail.com>:

> I use GROCER's ms_var function to estimate a single variable VAR model,
> and it estimates parameters as expected and described by the manual. I want
> to train and evaluate my model on different data sets to avoid bias from
> training and benchmarking on the same data set. How can this be done?
>
> For example consider data set A (month 1) and data set B (month 2) from a
> 2 month sample. I would like to train on month 1 and then benchmark on
> month 2.
>
> I use ms_var to train on data set A. It gives me estimated parameters and
> filtered regime probabilities. That works well. How can I use the trained
> parameters to then estimate on month 2 data?
>
> I'm aware of the ms_forecast function, but it seems to only forecast using
> the results from an estimator like ms_var(). The forecasting will then only
> be done on the same data as was used for estimating. I want to use the
> trained parameters to product estimates for a different data set.
>
> Thanks in advance. I really appreciate being able to use this software.
>
> -Brian
>
> --
> Brian Bouterse
>
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