From addfced3bc3439f536055bba8b06d07cc5ffe04f Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Tue, 12 Mar 2024 15:40:34 +0100
Subject: [PATCH] manual: clarify treatment of missing data in Inversion Filter
---
doc/manual/source/the-model-file.rst | 21 ++++++++++++++++-----
1 file changed, 16 insertions(+), 5 deletions(-)
diff --git a/doc/manual/source/the-model-file.rst b/doc/manual/source/the-model-file.rst
index ce6e09e7be..a47791f929 100644
--- a/doc/manual/source/the-model-file.rst
+++ b/doc/manual/source/the-model-file.rst
@@ -5590,11 +5590,22 @@ All of these elements are discussed in the following.
or the piecewise Kalman filter (default). An issue that can arise in the context of
estimation is a structural shock dropping out of the model in a particular regime.
For example, at the zero lower bound on interest rates, the monetary policy shock
- in the Taylor rule will not appear anymore. This may create a problem
- of stochastic singularity if there are then more observables than shocks. To
- avoid this issue, the data points for the zero interest rate should be set
- to NaN and the standard deviation of the associated shock set to 0 for the
- corresponding periods using the ``heteroskedastic_shocks`` block.
+ in the Taylor rule will not appear anymore. This may create a problem if there are
+ then more observables than shocks. The way to handle this issue depends on the type of filter used.
+ The first step is to set the data points for the zero interest rate period to NaN. For the piecewise
+ Kalman filter, the standard deviation of the associated shock needs to be set to 0 for the
+ corresponding periods using the ``heteroskedastic_shocks`` block. This avoids stochastic singularity.
+ However, this approach does not work for the inversion filter as the ``heteroskedastic_shocks`` block
+ does not do anything here. For the inversion filter, as many shocks as observables are required
+ at each point in time. Dynare assumes a one-to-one mapping between the declared shocks in
+ ``varexo`` and declared observables in ``varobs``. For example, if the second declared observable
+ is NaN in a given period, Dynare will drop the second declared shock.
+
+ .. warning:: If there are missing values, it is imperative for the inversion filter that the
+ declaration order of shocks and observables is conformable. Sticking with our example, if the nominal
+ interest is the second ``varobs`` and is set to NaN, the inversion filter will drop the second
+ declared shock. If that second declared shock is, e.g., a TFP shock, it will be dropped instead
+ of the intended monetary policy shock.
Note that models with unit roots will require the user to specify the ``diffuse_filter`` option as
otherwise Blanchard-Kahn errors will be triggered. For the piecewise Kalman filter, the
--
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