diff --git a/doc/dynare.texi b/doc/dynare.texi
index 2a13b153f789d4e330313eb0be9094e3bd148329..575b5acda42e44f9e4c1e9af050c08f432f7f517 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -5958,8 +5958,8 @@ model by finding the structural shocks that are needed to match the
 restricted paths. Consider the an augmented state space representation
 that stacks both predetermined and non-predetermined variables into a
 vector @math{y_{t}}:
- 
-@math{y_t=Ty_{t-1}+R\varepsilon_t} 
+
+@math{y_t=Ty_{t-1}+R\varepsilon_t}
 
 Both
 @math{y_t} and @math{\varepsilon_t} are split up into controlled and
@@ -5967,9 +5967,9 @@ uncontrolled ones to get:
 
 @math{y_t(contr\_vars)=Ty_{t-1}(contr\_vars)+R(contr\_vars,uncontr\_shocks)\varepsilon_t(uncontr\_shocks)
 +R(contr\_vars,contr\_shocks)\varepsilon_t(contr\_shocks)}
- 
+
 which can be solved algebraically for @math{\varepsilon_t(contr\_shocks)}.
- 
+
 Using these controlled shocks, the state-space representation can be used
 for forecasting. A few things need to be noted. First, it is assumed that
 controlled exogenous variables are fully under control of the policy
@@ -5985,21 +5985,17 @@ perfectly under the control of the policy-maker, they are nevertheless
 random and unforeseen shocks from the perspective of the households. That is,
 households are in each period surprised by the realization of a shock
 that keeps the controlled endogenous variables at their respective level.
-Fourth, due to the use of the above formula to compute the controlled
-exogenous variables, only relationships between controlled exogenous
-variables embedded in the matrix @math{R} are considered. This implies
-that any correlation information embedded in the covariance matrix of the
-@math{\varepsilon} as specified in the @code{shocks}-block are via
-estimated correlations or covariances is ignored as the controlled
-exogenous variables are assumed to be perfectly controlled without any
-interdependence. Thus, if you want to specify/preserve a correlation
-structure between controlled exogenous variables, you have to embedd that
-correlation stucture directly in the model-block by e.g. having the same
-shock enter different equations.
-
-
-This
-command has to be called after @code{estimation} of @code{stoch_simul}.
+Fourth, keep in mind that if the structural innovations are correlated,
+because the calibrated or estimated covariance matrix has non zero off
+diagonal elements, the results of the conditional forecasts will depend on
+the ordering of the innovations (as declared after @code{varexo}). As in VAR
+models, a Cholesky decomposition is used to factorize the covariance matrix
+and identify orthogonal impulses. It is preferable to declare the correlations
+in the @code{model} block (explicitly imposing the identification restrictions),
+unless you are satisfied with the implicit identification restrictions implied
+by the Cholesky decomposition.
+
+This command has to be called after @code{estimation} or @code{stoch_simul}.
 
 Use @code{conditional_forecast_paths} block to give the list of
 constrained endogenous, and their constrained future path.