diff --git a/doc/manual/source/bibliography.rst b/doc/manual/source/bibliography.rst
index 9e17ff619a4dba24360f6f1d9716e3e807fa29ce..b9aa1fb775bc893498d389a8a5411de7c097c423 100644
--- a/doc/manual/source/bibliography.rst
+++ b/doc/manual/source/bibliography.rst
@@ -65,7 +65,7 @@ Bibliography
 * Koopman, S. J. and J. Durbin (2003): “Filtering and Smoothing of State Vector for Diffuse State Space Models,” *Journal of Time Series Analysis*, 24(1), 85–98.
 * Kuntsevich, Alexei V. and Franz Kappel (1997): “SolvOpt - The solver for local nonlinear optimization problems (version 1.1, Matlab, C, FORTRAN)”, University of Graz, Graz, Austria.
 * Laffargue, Jean-Pierre (1990): “Résolution d’un modèle macroéconomique avec anticipations rationnelles”, *Annales d’Économie et Statistique*, 17, 97–119.
-* Liu, Jane and Mike West (2001): “Combined parameter and state estimation in simulation-based filtering”, in *Sequential Monte Carlo Methods in Practice*, Eds. Doucet, Freitas and Gordon, Springer Verlag.
+* Liu, Jane and Mike West (2001): “Combined parameter and state estimation in simulation-based filtering”, in *Sequential Monte Carlo Methods in Practice*, Eds. Doucet, Freitas and Gordon, Springer Verlag, Chapter 10, 197-223.
 * Murray, Lawrence M., Emlyn M. Jones and John Parslow (2013): “On Disturbance State-Space Models and the Particle Marginal Metropolis-Hastings Sampler”, *SIAM/ASA Journal on Uncertainty Quantification*, 1, 494–521.
 * Mutschler, Willi (2015): “Identification of DSGE models - The effect of higher-order approximation and pruning“, *Journal of Economic Dynamics & Control*, 56, 34-54.
 * Mutschler, Willi (2018): “Higher-order statistics for DSGE models”, *Econometrics and Statistics*, 6(C), 44-56.
diff --git a/doc/manual/source/the-model-file.rst b/doc/manual/source/the-model-file.rst
index 2740c246216f04963f44cb5e98f2069b488f1e49..f32173a739167d758e3e5ccbc9e5533a8b0de15c 100644
--- a/doc/manual/source/the-model-file.rst
+++ b/doc/manual/source/the-model-file.rst
@@ -6934,13 +6934,10 @@ observed variables.
 
            ``11``
 
-                This is not strictly speaking an optimization
-                algorithm. The (estimated) parameters are treated as
-                state variables and estimated jointly with the
-                original state variables of the model using a
-                nonlinear filter. The algorithm implemented in Dynare
-                is described in *Liu and West (2001)*, and works with
-                ``k`` order local approximations of the model.
+                Currently not in use. The Liu and West (2020) filter that 
+                used to be available under this option value is now triggered with 
+                ``posterior_sampling_method='online'``.
+                
 
            ``12``
 
@@ -7642,6 +7639,15 @@ observed variables.
                sampler proposed by *Waggoner, Wu and Zha (2016)* instead of the
                standard Random-Walk Metropolis-Hastings.
 
+           ``'online'``
+            
+                Instructs Dynare to treat the (estimated) parameters as
+                state variables and estimate them jointly with the
+                original state variables of the model using a
+                nonlinear filter. The algorithm implemented in Dynare
+                is described in *Liu and West (2001)*, and works with
+                ``k`` order local approximations of the model.
+
     .. option:: posterior_sampler_options = (NAME, VALUE, ...)
 
        A list of NAME and VALUE pairs. Can be used to set options for
@@ -8503,7 +8509,8 @@ observed variables.
 
            ``'liu_west_delta'``
 
-               Set the value for delta for the Liu/West online filter. Default: ``0.99``.
+               Set the value for delta for the Liu/West online filter (``posterior_sampling_method='online'``). 
+               Default: ``0.99``.
 
            ``'unscented_alpha'``