From 38e300b94e04f8bc2f16a79a32739bfd169c9c1b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Charybdis=29?=
<stepan@adjemian.eu>
Date: Sat, 16 Jan 2021 11:21:14 +0100
Subject: [PATCH] Merge #1786.
Do not compute cholesky decomposition of H if the matrix has zeros on
the diagonal.
---
matlab/initial_estimation_checks.m | 14 +++++++++-----
1 file changed, 9 insertions(+), 5 deletions(-)
diff --git a/matlab/initial_estimation_checks.m b/matlab/initial_estimation_checks.m
index c1bc6a0dcd..bec9b41ba0 100644
--- a/matlab/initial_estimation_checks.m
+++ b/matlab/initial_estimation_checks.m
@@ -41,7 +41,7 @@ function DynareResults = initial_estimation_checks(objective_function,xparam1,Dy
%singularity check
maximum_number_non_missing_observations=max(sum(~isnan(DynareDataset.data),2));
-if DynareOptions.order>1
+if DynareOptions.order>1
if any(any(isnan(DynareDataset.data)))
error('initial_estimation_checks:: particle filtering does not support missing observations')
end
@@ -54,9 +54,13 @@ if DynareOptions.order>1
if Model.H==0
error('initial_estimation_checks:: particle filtering requires measurement error on the observables')
else
- [~,flag]=chol(Model.H);
- if flag
- error('initial_estimation_checks:: the measurement error matrix must be positive definite')
+ if sum(diag(Model.H)>0)<length(DynareOptions.varobs)
+ error('initial_estimation_checks:: particle filtering requires as many measurement errors as observed variables')
+ else
+ [~,flag]=chol(Model.H);
+ if flag
+ error('initial_estimation_checks:: the measurement error matrix must be positive definite')
+ end
end
end
end
@@ -163,7 +167,7 @@ if ~isequal(DynareOptions.mode_compute,11) || ...
if info(1)==201
fprintf('initial_estimation_checks:: Initial covariance of the states is not positive definite. Try a different nonlinear_filter_initialization.\n')
error('initial_estimation_checks:: Initial covariance of the states is not positive definite. Try a different nonlinear_filter_initialization.')
- end
+ end
%reset options
DynareOptions.use_univariate_filters_if_singularity_is_detected=use_univariate_filters_if_singularity_is_detected_old;
else
--
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