Different length in conditional forecasts: 'plan' and 'condition'

[Hector Herrada]

Hi folks, I was wondering if there is a way to generate conditional forecasts on multiple variables but that we have different judgmental k future step lengths for each of these variables.

Thanks!

H

[Jaromir Benes]

Sure, just use a separate "condition" or "exogenize" command in the plan for each variable (or a group of variables with the same conditioning horizon).

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[Hector Herrada]

Hi Jaromir, thanks for your reply. The script would therefore look something like:

plann1       = plan(Model,Range_X1);

plann2       = plan(Model,Range_X2);

plann1       = condition(plann1,X1),Range_X1);

plann2       = condition(plann2,X2,Range_X2);

But how would the 'jforecast' syntax would look like?

condforecast = jforecast(Model,Data,...);

Thanks!

[Michael Johnston]

Close. Should look like this: 

p = plan(Model,Range_X1);

p = condition(p,'X1',Range_X1);

p = condition(p,'X2',Range_X2);

But how would the 'jforecast' syntax would look like?

condforecast = jforecast(Model,Data,forecast_range,'plan=',p);

[Hector Herrada]

what if Range_X2 is longer than Range_X1? The part that confuses me is when we create the plan with a shorter range and then input a longer range as a condition.

Thanks!

[Michael Johnston]

Typically I use a plan range which corresponds to the range of the entire forecast horizon. This is just a blank plan object. Then you can add particular periods over which you want to condition particular variables, or exogenise, etc. E.g. 

rang = startfcst:endfcst ;

p = plan(m,rang) ;

p = condition(p,'X1',x1_condition_range) ;

p = condition(p,'X2',x2_condition_range) ;

u = jforecast(m,d,rang,'plan=',p)

...

[Hector Herrada]

That makes sense. Thanks, Michael!