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Hi,

I am attempting to forecast a few variables (ratios) in a very simple manner - using a time trend and lag value of the variable. In the following code, a b c are groups with the variables in them. There are two requirements:
1. The forecasts of these variables (ratios) are constrained by the identity set out below. Basically, the ratios will sum up to 1. (Actually, I just realised that sum of the identity appears to be imposed, while some aren't. Is there a standard way of making sure the ratios sum up to 1 in the forecasts?)
2. The forecasts of these variables can't be negative. How can I go about setting up this constraint in the following code?
I had a look at this thread: http://forums.eviews.com/viewtopic.php?t=48. But, I am still not entirely sure how to add in this non-negativity constraint in my case.
Any help would be greatly appreciated. Thanks.

Hi,

Just wondering if anyone is able to help? I have a large number of identities in each model. But, I'm not entirely sure how to go about imposing the inequality as follows:

Mixed equality and inequality

A commonly encountered mixed type of restriction is to have all parameters non-negative and sum up to one


ai > 0 and a1 + a2 + ... + ak = 1


A reparameterization that satisfies these constraints is

Code: Select all
ai = exp( bi ) / ( exp( b1 ) + exp( b2 ) + ... + exp( bk ) )


where the bi parameters are unconstrained.


Any help would be great.

Thanks.

Where are the ai coming from in your original example?

Hi,

My ai's in this example are:

shares_a system - a1, a2, a3
shares_b system - b1, b2, b3, b4
shares_c system - c1, c2, c3, c4, c5

So, these are historical ratios I have calculated, and they add up to 1.

But, I wasn't sure how to ensure that when the system is solved, the forecasts of these ratios are all positive and add up to 1. The identities I have imposed appear to add most of them up to 1. But, some of them don't.

Any help would be great.

Can you post what you've currently done.

Hi,

Here's what I have done so far. The identity was one way I thought would ensure that the ratios sum up to 1 in the forecasts. But, I am doubtful now.

Here are the constraints I am hoping to implement:
- Forecast ratios in each group sum up to 1
- None of the forecast ratios are <1

Any help on how I can amend the code would be appreciated.