# There is 3 colinear relationships between the variables and the equations

**URL:** https://forum.dynare.org/t/there-is-3-colinear-relationships-between-the-variables-and-the-equations/24814
**Category:** Stochastic simulations
**Created:** [21 December 2023 19:32 UTC](https://forum.dynare.org/t/there-is-3-colinear-relationships-between-the-variables-and-the-equations/24814 "2023-12-21T19:32:40Z")
**Posts on this page:** 1
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### Author: ![jpfeifer](https://forum.dynare.org/user_avatar/forum.dynare.org/jpfeifer/32/5044_2.png) [@jpfeifer](https://forum.dynare.org/u/jpfeifer)
#### Post date: [22 December 2023 08:47 UTC](https://forum.dynare.org/t/there-is-3-colinear-relationships-between-the-variables-and-the-equations/24814/3 "2023-12-22T08:47:04Z")

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Why are you even doing a full exp()-substitution. It’s usually better to append auxiliary equations. See

> [@Question about understanding irfs in dynare](https://forum.dynare.org/t/question-about-understanding-irfs-in-dynare/10622/4):
>
> To be more precise: it depends on how the variables in your model are measured. As @stepan-a says, Dynare conducts a linearization. If you for example have a nonlinear model and your model variable is simply the level of output, the IRF will measure the difference between the variable and its steady state (or its mean at higher order). This is the case with [https://github.com/DynareTeam/dynare/blob/master/examples/example1.mod](https://github.com/DynareTeam/dynare/blob/master/examples/example1.mod) In contrast, if your model variable already measures percentage…

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