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Subject: Re: [Scilab-users] Using GROCER ms_var parameters for forecasting
From: Eric Dubois <grocer.toolbox () gmail ! com>
Date: 2015-02-19 20:43:50
Message-ID: CAGgDjFQKN9BvBrajOvPfCLkD+kdjv9hZMBkS2kneF6rNyBiHiQ () mail ! gmail ! com
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Dear Brian
You cannot perform forecasts with the results fo the function I sent you,
because these results are under a matrix form while ms_forecast needs a
results tlist (typed list). What is needed is therefore a results tlist
with all needed fields to make forecasts. You will find enclosed a new
ms_var_run function that makes that. What I have done is replacing the
results that are new in the results tlist estimated, while keeping all
invariant results (suach as estimated parameters, t-stats,...): I think I
have done it properly, but I cannot insure you that it is the case.
Starting for the previous example, replace:
--> [y_hat,resid,PR,PR_STT,PR_STL]=run_ms_var(r,'100*(log(
us_revu)-lagts(2,log(us_revu)))'
with:
-->newr=run_ms_var(r,'100*(log(us_revu)-lagts(2,log(us_revu)))'
and then make a forecast with:
--> rf=ms_forecast(newr,'2004m12')
Again, the function is rough and should be improved somehow.
Éric.
2015-02-19 14:28 GMT+01:00 Brian Bouterse <bmbouter@gmail.com>:
> Hi Eric,
>
> Thank you so much for the function. The verification step you demonstrate
> are convincing that the implementation produces the correct filtered
> probability result on the benchmark data. I've been able to reproduce your
> demo results, and also apply it to my own data set. This is great!
>
> There is one more thing that I'm not sure how to do for the single
> variable case. How can I take the results I have from run_ms_var() and use
> them with ms_forecast() to produce a single variable filtered estimate? The
> results I have are [y_hat,resid,PR,PR_STT,PR_STL]. I imagine this could
> be done using the following pseudocode:
>
> for each time step in PR_STT:
> select the regime with the highest filtered probability for this time
> step (ie: say regime N). This is like a maximum likelihood selection.
> select the autoregressive parameters for regime N from the original
> training step
> forecast the next time step using the autoregressive parameters using
> regime N
>
> This seems very similar to what ms_forecast() can do, but I'm not sure how
> to call ms_forecast given only the existence of parameters
> [y_hat,resid,PR,PR_STT,PR_STL]. Is this possible?
>
> Perhaps one of the variables [y_hat,resid,PR,PR_STT,PR_STL] already
> contains what I am looking for, but I want to be sure that it is based on
> the filtered probabilities and not considering data that comes later in the
> data set than the point of prediction. Does that make sense? In other words
> I want to predict the specific value at time t, and only consider data on
> the interval [0, t-1].
>
> Thanks again for everything you've done including writing this, helping
> me, responding so quickly, etc. This is really great.
>
> -Brian
>
>
>
> On Tue, Feb 17, 2015 at 3:50 PM, Eric Dubois <grocer.toolbox@gmail.com>
> wrote:
>
> > Dear Brian.
> >
> > 1) sorry, I made indeed a typo and wanted to speak about y_mat, x_mat and
> > z_mat.
> >
> > 2) I do not know exactly what you want, but you can calculate what you
> > want from the parameters and all other inputs
> >
> > 3) you will find attached a function run_ms_var that performs, I hope,
> > what you need: this function takes a results tlist from a ms_var execution
> > and a vector of endogenous variables to feed the VAR (your benchmark data).
> >
> > I have checked that if you give as endogenous variables exactly the same
> > variables as the one used for estimation, you recover the same yhat,
> > filtered probs, etc.
> >
> > To use the function, you have to save it in a folder, say c:/newms, and
> > run into Scilab
> > --> getd('c:/newms)
> >
> > To check what I mentionned above, run:
> > --> load(GROCERDIR+'\data\us_revu.dat')
> > --> bounds('1967m4','2004m2')
> > --> nb_states=2
> > --> switch_var=2 // variances are switching
> > --> var_opt=3 // heteroskedastik var-cov matrix
> >
> > --> r=ms_var('cte',3,'100*(log(us_revu)-lagts(2,log(us_revu)))',nb_states,switch_var,var_opt,'prt=initial;final','transf=stud')
> >
> > --> [y_hat,resid,PR,PR_STT,PR_STL]=run_ms_var(r,'100*(log(us_revu)-lagts(2,log(us_revu)))'
> >
> > --> PR_STT-r('filtered probs')
> >
> > The function is rather rough (no header, no options,...) and can be
> > improved, but I hope it answers your needs.
> >
> > Éric.
> >
> >
> >
> >
> > 2015-02-17 15:03 GMT+01:00 Brian Bouterse <bmbouter@gmail.com>:
> >
> > > Hi Eric,
> > >
> > > Thanks for the reply! Yes you understand my goals correctly, but one
> > > clarification: It would be better to have the estimated values directly
> > > instead of the filtered state probabilities. I usually get these with
> > > ms_forecast(r, n).
> > >
> > > I've been reading through the grocer code to determine how to write the
> > > function you suggest. I do need it sooner than a few weeks so I'm
> > > attempting to do it. It seems straightforward except for the y_hat, x_hat,
> > > and z_hat variables I need to provide to MSVAR_Filt.(). Here are some
> > > questions:
> > >
> > > 1) You say I need to feed MSVAR_Filt() with y_hat, x_hat, and z_hat, but
> > > the variables in the function signature for MSVAR_Filt read
> > > as y_mat,x_mat,z_mat. Did you mean y_mat or y_hat?
> > >
> > > 2) y_hat (2nd output) is an output of MSVAR_Filt(). The function
> > > comments say that is my estimated y. Is that the direct estimates that I am
> > > looking for?
> > >
> > > 3) I read through ms_var() to see how to derive the y_hat, x_hat, and
> > > z_hat variables that are needed, but I don't see any code in ms_var that
> > > derive these variables. Can you more specifically point out where the code
> > > is that shows the derivation of these matrices?
> > >
> > > Separate from those questions I am wondering what kind of bias is
> > > introduced if I use the filtered probabilities from ms_var? Could I use
> > > those instead of attempting to predict with data set A and evaluate with
> > > data set B. The reason I like the two data set methodology is that the
> > > training data (A) is separated from the evaluation data (B) so there can't
> > > be any bias in terms of measuring how the trained data generalizes when
> > > benchmarked on evaluation data because the training model never saw data
> > > set (B). Chapter 23 says the filtered probabilities only use data up until
> > > that point in time, but it uses estimates that were built from all
> > > information that is available. It seems biased to evaluate the residuals
> > > using filtered probabilities (or smoothed probabilities) because training
> > > and evaluating error on the same data set seems wrong. What do you think
> > > the right way is to use these tools to avoid bias when measuring error of
> > > model performance?
> > >
> > > Thanks for any information. Also is there any possibility for us to chat
> > > on IRC? I'm 'bmbouter' in #scilab on freenode if you want to chat there. It
> > > would probably be faster than e-mail.
> > >
> > > Thanks!
> > > Brian
> > >
> > >
> > > On Thu, Feb 12, 2015 at 3:44 PM, Eric Dubois <grocer.toolbox@gmail.com>
> > > wrote:
> > >
> > > > 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@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
> > > > >
> > > > > _______________________________________________
> > > > > users mailing list
> > > > > users@lists.scilab.org
> > > > > http://lists.scilab.org/mailman/listinfo/users
> > > > >
> > > > >
> > > >
> > > > _______________________________________________
> > > > users mailing list
> > > > users@lists.scilab.org
> > > > http://lists.scilab.org/mailman/listinfo/users
> > > >
> > > >
> > >
> > >
> > > --
> > > Brian Bouterse
> > >
> > > _______________________________________________
> > > users mailing list
> > > users@lists.scilab.org
> > > http://lists.scilab.org/mailman/listinfo/users
> > >
> > >
> >
> > _______________________________________________
> > users mailing list
> > users@lists.scilab.org
> > http://lists.scilab.org/mailman/listinfo/users
> >
> >
>
>
> --
> Brian Bouterse
>
> _______________________________________________
> users mailing list
> users@lists.scilab.org
> http://lists.scilab.org/mailman/listinfo/users
>
>
[Attachment #5 (text/html)]
<div dir="ltr">Dear Brian<div><br></div><div>You cannot perform forecasts with the \
results fo the function I sent you, because these results are under a matrix form \
while ms_forecast needs a results tlist (typed list). What is needed is therefore a \
results tlist with all needed fields to make forecasts. You will find enclosed a new \
ms_var_run function that makes that. What I have done is replacing the results that \
are new in the results tlist estimated, while keeping all invariant results (suach as \
estimated parameters, t-stats,...): I think I have done it properly, but I cannot \
insure you that it is the case.</div><div><br></div><div>Starting for the previous \
example, replace:</div><div><span style="font-size:13px">--> \
[y_hat,resid,PR,PR_STT,PR_</span><span \
style="font-size:13px">STL]=run_ms_var(r,'100*(log(</span><span \
style="font-size:13px">us_revu)-lagts(2,log(us_revu))</span><span \
style="font-size:13px">)'</span><br></div><div><span \
style="font-size:13px"><br></span></div><div><span \
style="font-size:13px">with:</span></div><div><span \
style="font-size:13px">-->newr</span><span \
style="font-size:13px">=run_ms_var(r,'100*(log(</span><span \
style="font-size:13px">us_revu)-lagts(2,log(us_revu))</span><span \
style="font-size:13px">)'</span><span \
style="font-size:13px"><br></span></div><div><br></div><div>and then make a forecast \
with:</div><div>--> \
rf=ms_forecast(newr,'2004m12')</div><div><br></div><div>Again, the function \
is rough and should be improved somehow. </div><div><br></div><div>Éric. \
</div></div><div class="gmail_extra"><br><div class="gmail_quote">2015-02-19 14:28 \
GMT+01:00 Brian Bouterse <span dir="ltr"><<a href="mailto:bmbouter@gmail.com" \
target="_blank">bmbouter@gmail.com</a>></span>:<br><blockquote class="gmail_quote" \
style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div \
dir="ltr">Hi Eric,<div><br></div><div>Thank you so much for the function. The \
verification step you demonstrate are convincing that the implementation produces the \
correct filtered probability result on the benchmark data. I've been able to \
reproduce your demo results, and also apply it to my own data set. This is \
great!</div><div><br></div><div>There is one more thing that I'm not sure how to \
do for the single variable case. How can I take the results I have from run_ms_var() \
and use them with ms_forecast() to produce a single variable filtered estimate? The \
results I have are <span \
style="font-size:13px">[y_hat,resid,PR,PR_STT,PR_</span><span \
style="font-size:13px">STL]. I imagine this could be done using the following \
pseudocode:</span></div><div><span style="font-size:13px"><br></span></div><div><span \
style="font-size:13px">for each time step in PR_STT:</span></div><div><span \
style="font-size:13px"> select the regime with the highest filtered probability \
for this time step (ie: say regime N). This is like a maximum likelihood \
selection.</span></div><div><span style="font-size:13px"> select the \
autoregressive parameters for regime N from the original training \
step</span></div><div><span style="font-size:13px"> forecast the next time step \
using the autoregressive parameters using regime \
N</span></div><div><br></div><div>This seems very similar to what ms_forecast() can \
do, but I'm not sure how to call ms_forecast given only the existence of \
parameters <span style="font-size:13px">[y_hat,resid,PR,PR_STT,PR_</span><span \
style="font-size:13px">STL]. Is this possible?</span></div><div><span \
style="font-size:13px"><br></span></div><div><span style="font-size:13px">Perhaps one \
of the variables </span><span \
style="font-size:13px">[y_hat,resid,PR,PR_STT,PR_</span><span \
style="font-size:13px">STL] already contains what I am looking for, but I want to be \
sure that it is based on the filtered probabilities and not considering data that \
comes later in the data set than the point of prediction. Does that make sense? In \
other words I want to predict the specific value at time t, and only consider data on \
the interval [0, t-1].</span></div><div><span \
style="font-size:13px"><br></span></div><div><span style="font-size:13px">Thanks \
again for everything you've done including writing this, helping me, responding \
so quickly, etc. This is really great.</span></div><span class="HOEnZb"><font \
color="#888888"><div><br></div><div>-Brian</div><div><br></div><div><br></div></font></span></div><div \
class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div \
class="gmail_quote">On Tue, Feb 17, 2015 at 3:50 PM, Eric Dubois <span \
dir="ltr"><<a href="mailto:grocer.toolbox@gmail.com" \
target="_blank">grocer.toolbox@gmail.com</a>></span> wrote:<br><blockquote \
class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc \
solid;padding-left:1ex"><div dir="ltr">Dear Brian.<div><br></div><div>1) sorry, I \
made indeed a typo and wanted to speak about y_mat, x_mat and \
z_mat.</div><div><br></div><div>2) I do not know exactly what you want, but you can \
calculate what you want from the parameters and all other \
inputs</div><div><br></div><div>3) you will find attached a function run_ms_var that \
performs, I hope, what you need: this function takes a results tlist from a ms_var \
execution and a vector of endogenous variables to feed the VAR (your benchmark \
data).</div><div><br></div><div>I have checked that if you give as endogenous \
variables exactly the same variables as the one used for estimation, you recover the \
same yhat, filtered probs, etc.</div><div><br></div><div>To use the function, you \
have to save it in a folder, say c:/newms, and run into Scilab</div><div>--> \
getd('c:/newms)</div><div><br></div><div>To check what I mentionned above, \
run:</div><div>--> load(GROCERDIR+'\data\us_revu.dat')</div><div>--> \
bounds('1967m4','2004m2')</div><div>--> \
nb_states=2</div><div>--> switch_var=2 // variances are \
switching</div><div>--> var_opt=3 // heteroskedastik var-cov \
matrix</div><div>--> \
r=ms_var('cte',3,'100*(log(us_revu)-lagts(2,log(us_revu)))',nb_states, \
switch_var,var_opt,'prt=initial;final','transf=stud')<br></div><div>--> \
[y_hat,resid,PR,PR_STT,PR_STL]=run_ms_var(r,'100*(log(us_revu)-lagts(2,log(us_revu)))'</div><div>--> \
PR_STT-r('filtered probs')</div><div><br></div><div>The function is rather \
rough (no header, no options,...) and can be improved, but I hope it answers your \
needs.</div><div><br></div><div>Éric.</div><div><br></div><div><br></div><div><br></div></div><div><div><div \
class="gmail_extra"><br><div class="gmail_quote">2015-02-17 15:03 GMT+01:00 Brian \
Bouterse <span dir="ltr"><<a href="mailto:bmbouter@gmail.com" \
target="_blank">bmbouter@gmail.com</a>></span>:<br><blockquote class="gmail_quote" \
style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div \
dir="ltr">Hi Eric,<div><br></div><div>Thanks for the reply! Yes you understand my \
goals correctly, but one clarification: It would be better to have the estimated \
values directly instead of the filtered state probabilities. I usually get these with \
ms_forecast(r, n).</div><div><br></div><div>I've been reading through the grocer \
code to determine how to write the function you suggest. I do need it sooner than a \
few weeks so I'm attempting to do it. It seems straightforward except for the \
y_hat, x_hat, and z_hat variables I need to provide to MSVAR_Filt.(). Here are some \
questions:</div><div><br></div><div>1) You say I need to feed MSVAR_Filt() with \
y_hat, x_hat, and z_hat, but the variables in the function signature for MSVAR_Filt \
read as y_mat,x_mat,z_mat. Did you mean y_mat or y_hat?</div><div><br></div><div>2) \
y_hat (2nd output) is an output of MSVAR_Filt(). The function comments say that is my \
estimated y. Is that the direct estimates that I am looking \
for?</div><div><br></div><div>3) I read through ms_var() to see how to derive the \
y_hat, x_hat, and z_hat variables that are needed, but I don't see any code in \
ms_var that derive these variables. Can you more specifically point out where the \
code is that shows the derivation of these \
matrices?</div><div><br></div><div>Separate from those questions I am wondering what \
kind of bias is introduced if I use the filtered probabilities from ms_var? Could I \
use those instead of attempting to predict with data set A and evaluate with data set \
B. The reason I like the two data set methodology is that the training data (A) is \
separated from the evaluation data (B) so there can't be any bias in terms of \
measuring how the trained data generalizes when benchmarked on evaluation data \
because the training model never saw data set (B). Chapter 23 says the filtered \
probabilities only use data up until that point in time, but it uses estimates that \
were built from all information that is available. It seems biased to evaluate the \
residuals using filtered probabilities (or smoothed probabilities) because training \
and evaluating error on the same data set seems wrong. What do you think the right \
way is to use these tools to avoid bias when measuring error of model \
performance?</div><div><br></div><div>Thanks for any information. Also is there any \
possibility for us to chat on IRC? I'm 'bmbouter' in #scilab on freenode \
if you want to chat there. It would probably be faster than \
e-mail.</div><div><br></div><div>Thanks!</div><div>Brian</div><div><br></div></div><div \
class="gmail_extra"><div><div><br><div class="gmail_quote">On Thu, Feb 12, 2015 at \
3:44 PM, Eric Dubois <span dir="ltr"><<a href="mailto:grocer.toolbox@gmail.com" \
target="_blank">grocer.toolbox@gmail.com</a>></span> wrote:<br><blockquote \
class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc \
solid;padding-left:1ex"><div dir="ltr">Dear Brian.<div><br></div><div>If I have well \
understood, you want:</div><div>- to estimate a ms_var model on a subset of your \
dataset;</div><div>- recover the estimated parameters;</div><div>- and calculate the \
filtered state probabilities on the other part of your dataset with these \
parameters.</div><div><br></div><div>This can be done:</div><div>- the function \
MSVAR_Filt calculates among other the filetered probabilities (5th \
output);</div><div>- 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);</div><div><br></div><div>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).</div><div><br></div><div>If you are not too \
in a hurry, I can write the function that gathers all these operations within a few \
weeks.</div><div><br></div><div>Éric.</div></div><div class="gmail_extra"><br><div \
class="gmail_quote"><div><div>2015-02-12 16:56 GMT+01:00 Brian Bouterse <span \
dir="ltr"><<a href="mailto:bmbouter@gmail.com" \
target="_blank">bmbouter@gmail.com</a>></span>:<br></div></div><blockquote \
class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc \
solid;padding-left:1ex"><div><div><div dir="ltr">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?<div><br></div><div>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.</div><div><br></div><div>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?</div><div><br></div><div>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.</div><div><br></div><div>Thanks in advance. I really appreciate being able to \
use this software.</div><span><font \
color="#888888"><div><br></div><div>-Brian</div><div><div><div><div><div><div><br></div>-- \
<br><div><div dir="ltr">Brian Bouterse<br></div></div> \
</div></div></div></div></div></font></span></div> \
<br></div></div>_______________________________________________<br> users mailing \
list<br> <a href="mailto:users@lists.scilab.org" \
target="_blank">users@lists.scilab.org</a><br> <a \
href="http://lists.scilab.org/mailman/listinfo/users" \
target="_blank">http://lists.scilab.org/mailman/listinfo/users</a><br> \
<br></blockquote></div><br></div> \
<br>_______________________________________________<br> users mailing list<br>
<a href="mailto:users@lists.scilab.org" \
target="_blank">users@lists.scilab.org</a><br> <a \
href="http://lists.scilab.org/mailman/listinfo/users" \
target="_blank">http://lists.scilab.org/mailman/listinfo/users</a><br> \
<br></blockquote></div><br><br clear="all"><div><br></div></div></div><span><font \
color="#888888">-- <br><div><div dir="ltr">Brian Bouterse<br></div></div> \
</font></span></div> <br>_______________________________________________<br>
users mailing list<br>
<a href="mailto:users@lists.scilab.org" \
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