From e70a8d8fe1d3ad6823859d030007bfcc497e9547 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Charybdis=29?=
<stepan@adjemian.eu>
Date: Sun, 16 Feb 2020 21:07:58 +0100
Subject: [PATCH] Updated reference manual wrt the nonlinear filters at higher
order.
(cherry picked from commit e5dca48abb5b3a071e9594f53729ba99b45ab4da)
---
doc/manual/source/the-model-file.rst | 18 ++++++++----------
1 file changed, 8 insertions(+), 10 deletions(-)
diff --git a/doc/manual/source/the-model-file.rst b/doc/manual/source/the-model-file.rst
index efe7df7b87..096ebbf34b 100644
--- a/doc/manual/source/the-model-file.rst
+++ b/doc/manual/source/the-model-file.rst
@@ -4879,10 +4879,8 @@ block decomposition of the model (see :opt:`block`).
the computed mode for each estimated parameter in turn. This is
helpful to diagnose problems with the optimizer. Note that for
``order>1`` the likelihood function resulting from the particle
- filter is not differentiable anymore due to random chatter
- introduced by selecting different particles for different
- parameter values. For this reason, the ``mode_check`` plot may
- look wiggly.
+ filter is not differentiable anymore due to the resampling
+ step. For this reason, the ``mode_check`` plot may look wiggly.
.. option:: mode_check_neighbourhood_size = DOUBLE
@@ -5794,12 +5792,12 @@ block decomposition of the model (see :opt:`block`).
.. option:: order = INTEGER
- Order of approximation, either ``1`` or ``2``. When equal to
- ``2``, the likelihood is evaluated with a particle filter based
- on a second order approximation of the model (see
- *Fernandez-Villaverde and Rubio-Ramirez (2005)*). Default is
- ``1``, i.e. the likelihood of the linearized model is evaluated
- using a standard Kalman filter.
+ Order of approximation around the deterministic steady
+ state. When greater than 1, the likelihood is evaluated with a
+ particle or nonlinear filter (see *Fernandez-Villaverde and
+ Rubio-Ramirez (2005)*). Default is ``1``, i.e. the likelihood
+ of the linearized model is evaluated using a standard Kalman
+ filter.
.. option:: irf = INTEGER
--
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