diff --git a/doc/dynare.texi b/doc/dynare.texi
index 90a005e21..b5ae1eae4 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -5318,7 +5318,15 @@ missing observations.
 
 @item endogenous_prior
 Use endogenous priors as in @cite{Christiano, Trabandt and Walentin
-(2011)}.
+(2011)}. 
+The procedure is motivated by sequential Bayesian learning.  Starting from independent initial priors on the parameters, 
+specified in the @code{estimated_params}-block, the standard deviations observed in a "pre-sample", 
+taken to be the actual sample, are used to update the initial priors. Thus, the product of the initial 
+priors and the pre-sample likelihood of the standard deviations of the observables is used as the new prior 
+(for more information, see the technical appendix of @cite{Christiano, Trabandt and Walentin (2011)}). 
+This procedure helps in cases where the regular posterior estimates, which minimize in-sample forecast 
+errors, result in a large overprediction 
+of model variable variances (a statistic that is not explicitly targeted, but often of particular interest to researchers).
 
 @item use_univariate_filters_if_singularity_is_detected = @var{INTEGER}
 @anchor{use_univariate_filters_if_singularity_is_detected}
diff --git a/matlab/resol.m b/matlab/resol.m
index 92c775806..a6a8bc0c8 100644
--- a/matlab/resol.m
+++ b/matlab/resol.m
@@ -4,7 +4,7 @@ function [dr,info,M,options,oo] = resol(check_flag,M,options,oo)
 %! @deftypefn {Function File} {[@var{dr},@var{info},@var{M},@var{options},@var{oo}] =} resol (@var{check_flag},@var{M},@var{options},@var{oo})
 %! @anchor{resol}
 %! @sp 1
-%! Computes first and second order reduced form of the DSGE model.
+%! Computes the perturbation-based decisions rules of the DSGE model (orders 1 to 3).
 %! @sp 2
 %! @strong{Inputs}
 %! @sp 1
diff --git a/matlab/stochastic_solvers.m b/matlab/stochastic_solvers.m
index 64b9b2c81..d8374c48f 100644
--- a/matlab/stochastic_solvers.m
+++ b/matlab/stochastic_solvers.m
@@ -1,7 +1,7 @@
 function [dr,info] = stochastic_solvers(dr,task,M_,options_,oo_)
 % function [dr,info,M_,options_,oo_] = stochastic_solvers(dr,task,M_,options_,oo_)
-% computes the reduced form solution of a rational expectation model (first or second order
-% approximation of the stochastic model around the deterministic steady state). 
+% computes the reduced form solution of a rational expectations model (first, second or third 
+% order approximation of the stochastic model around the deterministic steady state). 
 %
 % INPUTS
 %   dr         [matlab structure] Decision rules for stochastic simulations.