Document variance decomposition with measurement error.
Manually cherry-picked from a1222a1d1b
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@ -3151,17 +3151,25 @@ Computing the stochastic solution
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The results are stored in
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``oo_.conditional_variance_decomposition`` (see
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:mvar:`oo_.conditional_variance_decomposition`). The variance
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decomposition is only conducted, if theoretical moments are
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requested, i.e. using the ``periods=0`` option. In case of
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``order=2``, Dynare provides a second-order accurate
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:mvar:`oo_.conditional_variance_decomposition`). In the
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presence of measurement error, the
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``oo_.conditional_variance_decomposition`` field will contain
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the variance contribution after measurement error has been
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taken out, i.e. the decomposition will be conducted of the
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actual as opposed to the measured variables. The variance
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decomposition of the measured variables will be stored in
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``oo_.conditional_variance_decomposition_ME`` (see
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:mvar:`oo_.conditional_variance_decomposition_ME`). The
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variance decomposition is only conducted, if theoretical
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moments are requested, *i.e.* using the ``periods=0``-option.
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In case of ``order=2``, Dynare provides a second-order accurate
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approximation to the true second moments based on the linear
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terms of the second-order solution (see *Kim, Kim, Schaumburg
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and Sims (2008)*). Note that the unconditional variance
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decomposition (i.e. at horizon infinity) is automatically
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conducted if theoretical moments are requested and if
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``nodecomposition`` is not set (see
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:mvar:`oo_.variance_decomposition`)
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terms of the second-order solution (see *Kim, Kim,
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Schaumburg and Sims (2008)*). Note that the unconditional
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variance decomposition *i.e.* at horizon infinity) is
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automatically conducted if theoretical moments are requested
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and if ``nodecomposition`` is not set (see
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:mvar:`oo_.variance_decomposition`).
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.. option:: pruning
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@ -3402,7 +3410,7 @@ Computing the stochastic solution
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``oo_.gamma{nar+2}``
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Unconditional variance decomposition, see
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mvar:`oo_.variance_decomposition`.
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:mvar:`oo_.variance_decomposition`.
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``oo_.gamma{nar+3}``
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@ -3415,14 +3423,33 @@ Computing the stochastic solution
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.. matvar:: oo_.variance_decomposition
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|br| After a run of ``stoch_simul`` when requesting theoretical moments
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(``periods=0``), contains a matrix with the result of the
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|br| After a run of ``stoch_simul`` when requesting theoretical
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moments (``periods=0``), contains a matrix with the result of the
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unconditional variance decomposition (i.e. at horizon
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infinity). The first dimension corresponds to the endogenous
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variables (in the order of declaration) and the second dimension
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corresponds to exogenous variables (in the order of
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declaration). Numbers are in percent and sum up to 100 across
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columns.
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variables (in the order of declaration after the command or in
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``M_.endo_names``) and the second dimension corresponds to
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exogenous variables (in the order of declaration). Numbers are in
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percent and sum up to 100 across columns. In the presence of
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measurement error, the field will contain the variance
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contribution after measurement error has been taken out, *i.e.*
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the decomposition will be conducted of the actual as opposed to
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the measured variables.
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.. matvar:: oo_.variance_decomposition_ME
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|br| Field set after a run of ``stoch_simul`` when requesting
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theoretical moments (``periods=0``) if measurement error is
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present. It is similar to :mvar:`oo_.variance_decomposition`, but
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the decomposition will be conducted of the measured variables. The
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field contains a matrix with the result of the unconditional
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variance decomposition (*i.e.* at horizon infinity). The first
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dimension corresponds to the observed endoogenous variables (in
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the order of declaration after the command) and the second
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dimension corresponds to exogenous variables (in the order of
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declaration), with the last column corresponding to the
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contribution of measurement error. Numbers are in percent and sum
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up to 100 across columns.
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.. matvar:: oo_.conditional_variance_decomposition
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@ -3431,8 +3458,28 @@ Computing the stochastic solution
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three-dimensional array with the result of the decomposition. The
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first dimension corresponds to forecast horizons (as declared with
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the option), the second dimension corresponds to endogenous
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variables (in the order of declaration), the third dimension
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corresponds to exogenous variables (in the order of declaration).
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variables (in the order of declaration after the command or in
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``M_.endo_names`` if not specified), the third dimension
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corresponds to exogenous variables (in the order of
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declaration). In the presence of measurement error, the field will
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contain the variance contribution after measurement error has been
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taken out, *i.e.* the decomposition will be conductedof the actual
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as opposed to the measured variables.
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.. matvar:: oo_.conditional_variance_decomposition_ME
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|br| Field set after a run of ``stoch_simul`` with the
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``conditional_variance_decomposition`` option if measurement error
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is present. It is similar to
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:mvar:`oo_.conditional_variance_decomposition`, but the
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decomposition will be conducted of the measured variables. It
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contains a three-dimensional array with the result of the
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decomposition. The first dimension corresponds to forecast
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horizons (as declared with the option), the second dimension
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corresponds to observed endogenous variables (in the order of
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declaration), the third dimension corresponds to exogenous
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variables (in the order of declaration), with the last column
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corresponding to the contribution of the measurement error.
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.. matvar:: oo_.contemporaneous_correlation
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@ -5309,9 +5356,15 @@ block decomposition of the model (see :opt:`block`).
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:math:`var(y_{t+k}\vert t)`. For period 1, the conditional
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variance decomposition provides the decomposition of the
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effects of shocks upon impact. The results are stored in
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``oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition``,
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but currently there is no displayed output. Note that this
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option requires the ``moments_varendo`` to be specified.
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``oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition``.. Note
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that this option requires the option ``moments_varendo`` to be
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specified. In the presence of measurement error, the field will
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contain the variance contribution after measurement error has
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been taken out, *i.e.* the decomposition will be conducted of the
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actual as opposed to the measured variables. The variance
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decomposition of the measured variables will be stored in
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``oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecompositionME``.
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.. option:: filtered_vars
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@ -6291,8 +6344,21 @@ block decomposition of the model (see :opt:`block`).
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``ConditionalVarianceDecomposition``
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Only if the ``conditional_variance_decomposition`` option has
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been specified.
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Only if the ``conditional_variance_decomposition``
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option has been specified. In the presence of
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measurement error, the field will contain the variance
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contribution after measurement error has been taken
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out, i.e. the decomposition will be conducted of the
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actual as opposed to the measured variables.
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``ConditionalVarianceDecompositionME``
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Only if the ``conditional_variance_decomposition``
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option has been specified. Same as
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``ConditionalVarianceDecomposition``, but contains the
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decomposition of the measured as opposed to the actual
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variable. The joint contribution of the measurement
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error will be saved in a field names ``ME``.
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.. matvar:: oo_.posterior_density
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