# Stochastic Singularity and Variable correlation **URL:** https://forum.dynare.org/t/stochastic-singularity-and-variable-correlation/14492 **Category:** Estimation **Created:** [18 September 2019 20:45 UTC](https://forum.dynare.org/t/stochastic-singularity-and-variable-correlation/14492 "2019-09-18T20:45:55Z") **Posts on this page:** 1 **Showing post:** 4
### 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 September 2019 06:57 UTC](https://forum.dynare.org/t/stochastic-singularity-and-variable-correlation/14492/4 "2019-09-22T06:57:23Z")
1. See e.g. > [@Problems with identification for a SMOE (As in Gali ch. 7)](https://forum.dynare.org/t/problems-with-identification-for-a-smoe-as-in-gali-ch-7/4672/14): > > It seems to me you are simply neglecting the parameter dependence. Your calibration only once updates the other parameters depending on the estimated one. That’s why you should use model-local variables (the ones with the pound operator) or a steady state file. See Remark 4 (Parameter dependence and the use of model-local variables) in Pfeifer(2013): “A Guide to Specifying Observation Equations for the Estimation of DSGE Models” [sites.google.com/site/pfeiferec … ations.pdf](https://sites.google.com/site/pfeiferecon/Pfeifer_2013_Observation_Equations.pdf). For example, you est… 1. Your estimates would not improve if you add those particular variables. The reason is that those variables do not contain any additional information relative to the ones already used from the perspective of the model. That is the whole point of having a linear combination of observables in your model. --- _[View the full topic](https://forum.dynare.org/t/stochastic-singularity-and-variable-correlation/14492)._