Merge remote-tracking branch 'houtanb/master'
commit
74334df12a
638
doc/dynare.texi
638
doc/dynare.texi
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@ -817,6 +817,7 @@ internals --test particle/local_state_iteration
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* Forecasting::
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* Forecasting::
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* Optimal policy::
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* Optimal policy::
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* Sensitivity and identification analysis::
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* Sensitivity and identification analysis::
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* Markov-switching SBVAR::
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* Displaying and saving results::
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* Displaying and saving results::
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* Macro-processing language::
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* Macro-processing language::
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* Misc commands::
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* Misc commands::
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@ -855,6 +856,10 @@ mutually exclusive arguments are separated by vertical bars: @samp{|};
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@var{DOUBLE} indicates a double precision number. The following syntaxes
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@var{DOUBLE} indicates a double precision number. The following syntaxes
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are valid: @code{1.1e3}, @code{1.1E3}, @code{1.1d3}, @code{1.1D3};
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are valid: @code{1.1e3}, @code{1.1E3}, @code{1.1d3}, @code{1.1D3};
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@item
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@var{NUMERICAL_VECTOR} indicates a vector of numbers separated by spaces,
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enclosed by square brackets;
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@item
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@item
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@var{EXPRESSION} indicates a mathematical expression valid outside the
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@var{EXPRESSION} indicates a mathematical expression valid outside the
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model description (@pxref{Expressions});
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model description (@pxref{Expressions});
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@ -5303,6 +5308,634 @@ Specify the parameter set to use. Default: @code{prior_mean}
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@end deffn
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@end deffn
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@node Markov-switching SBVAR
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@section Markov-switching SBVAR
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Given a list of variables, observed variables and a data file, Dynare
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can be used to solve a Markov-switching SBVAR model according to
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@cite{Sims, Waggoner and Zha (2008)}. Having done this, you can create
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forecasts and compute the marginal data density, regime probabilities,
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IRFs, and variance decomposition of the model.
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The commands have been modularized, allowing for multiple calls to the
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same command within a @code{<mod_file>.mod} file. The default is to use
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@code{<mod_file>} to tag the input (output) files used (produced) by the
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program. Thus, to call any command more than once within a
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@code{<mod_file>.mod} file, you must use the @code{*_tag} options
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described below.
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@anchor{markov_switching}
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@deffn Command markov_switching (@var{OPTIONS}@dots{});
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@descriptionhead
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Declares the Markov state variable information of a Markov-switching
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SBVAR model.
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@optionshead
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@table @code
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@item chain = @var{INTEGER}
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@anchor{ms_chain} The Markov chain. Default: @code{none}
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@item state = @var{INTEGER}
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This state has duration equal to @code{duration}. Exactly one of
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@code{state} and @code{number_of_states} must be passed. Default:
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@code{none}
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@item number_of_states = @var{INTEGER}
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Total number of states. Implies that all states have the same
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duration. Exactly one of @code{state} and @code{number_of_states} must
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be passed. Default: @code{none}
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@item duration = @var{DOUBLE} | @code{inf}
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The duration of the state or states. Default: @code{none}
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@end table
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@end deffn
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@anchor{svar}
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@deffn Command svar (@var{OPTIONS}@dots{});
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@descriptionhead
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Each Makov chain can control the switching of a set of parameters. We
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allow the parameters to be divided equation by equation and by variance
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or slope and intercept.
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@optionshead
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@table @code
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@item coefficients
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Specifies that only the slope and intercept in the given equations are
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controlled by the given chain. One, but not both, of
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@code{coefficients} or @code{variances} must appear. Default:
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@code{none}
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@item variances
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Specifies that only variances in the given equations are controlled by
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the given chain. One, but not both, of @code{coefficients} or
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@code{variances} must appear. Default: @code{none}
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@item equations
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Defines the equation controlled by the given chain. If not specificed,
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then all equations are controlled by @code{chain}. Default: @code{none}
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@item chain = @var{INTEGER}
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Specifies a Markov chain defined by @ref{markov_switching}. Default:
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@code{none}
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@end table
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@end deffn
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@anchor{ms_estimation}
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@deffn Command ms_estimation (@var{OPTIONS}@dots{});
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@descriptionhead
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Triggers the creation of an initialization file for, and the estimation
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of, a Markov-switching SBVAR model. At the end of the run, the
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@math{A^0}, @math{A^+}, @math{Q} and @math{\zeta} matrices are contained
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in the @code{oo_.ms} structure.
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@optionshead
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@customhead{General Options}
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@table @code
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@item file_tag = @var{FILENAME}
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The portion of the filename associated with this run. This will create
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the model initialization file, @code{init_<file_tag>.dat}. Default:
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@code{<mod_file>}
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@item output_file_tag = @var{FILENAME}
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The portion of the output filename that will be assigned to this run.
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This will create, among other files,
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@code{est_final_<output_file_tag>.out},
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@code{est_intermediate_<output_file_tag>.out}. Default:
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@code{<file_tag>}
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@item no_create_init
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Do not create an initialization file for the model. Passing this option
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will cause the @i{Initialization Options} to be ignored. Further, the
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model will be generated from the output files associated with the
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previous estimation run (@i{i.e.} @code{est_final_<file_tag>.out},
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@code{est_intermediate_<file_tag>.out} or @code{init_<file_tag>.dat},
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searched for in sequential order). This functionality can be useful for
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continuing a previous estimation run to ensure convergence was reached
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or for reusing an initialization file. NB: If this option is not passed,
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the files from the previous estimation run will be overwritten. Default:
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@code{off} (@i{i.e.} create initialization file)
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@end table
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@customhead{Initialization Options}
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@table @code
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@item coefficients_prior_hyperparameters = [@var{DOUBLE1} @var{DOUBLE2} @var{DOUBLE3} @var{DOUBLE4} @var{DOUBLE5} @var{DOUBLE6}]
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Sets the hyper parameters for the model. The six elements of the
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argument vector have the following interpretations:
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@table @code
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@item Position
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@code{Interpretation}
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@item 1
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Overall tightness for @math{A^0} and @math{A^+}
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@item 2
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Relative tightness for @math{A^+}
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@item 3
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Relative tightness for the constant term
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@item 4
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Tightness on lag decay (range: 1.2 - 1.5); a faster decay produces
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better inflation process
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@item 5
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Weight on nvar sums of coeffs dummy observations (unit roots)
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@item 6
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Weight on single dummy initial observation including constant
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@end table
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Default: @code{[1.0 1.0 0.1 1.2 1.0 1.0]}
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@item freq = @var{INTEGER} | @code{monthly} | @code{quarterly} | @code{yearly}
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Frequency of the data (@i{e.g.} @code{monthly}, @code{12}). Default:
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@code{4}
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@item initial_year = @var{INTEGER}
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The first year of data. Default: @code{none}
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@item initial_subperiod = @var{INTEGER}
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The first period of data (@i{i.e.} for quarterly data, an integer in
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[@code{1,4}]). Default: @code{1}
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@item final_year = @var{INTEGER}
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The last year of data. Default: @code{none}
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@item final_subperiod = @var{INTEGER}
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The final period of data (@i{i.e.} for monthly data, an integer in
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[@code{1,12}]. Default: @code{4}
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@item datafile = @var{FILENAME}
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@xref{datafile}.
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@item xls_sheet = @var{NAME}
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@xref{xls_sheet}.
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@item xls_range = @var{RANGE}
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@xref{xls_range}.
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@item nlags = @var{INTEGER}
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The number of lags in the model. Default: @code{1}
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@item cross_restrictions
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Use cross @math{A^0} and @math{A^+} restrictions. Default: @code{off}
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@item contemp_reduced_form
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Use contemporaneous recursive reduced form. Default: @code{off}
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@item no_bayesian_prior = @var{INTEGER}
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Do not use bayesian prior. Default: @code{off} (@i{i.e.} use bayesian
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prior)
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@item alpha = @var{INTEGER}
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Alpha value for squared time-varying structural shock lambda. Default:
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@code{1}
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@item beta = @var{INTEGER}
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Beta value for squared time-varying structural shock lambda. Default:
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@code{1}
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@item gsig2_lmdm = @var{INTEGER}
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The variance for each independent @math{\lambda} parameter under
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@code{SimsZha} restrictions. Default: @code{50^2}
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@item specification = @code{sims_zha} | @code{none}
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This controls how restrictions are imposed to reduce the number of
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parameters. Default: @code{Random Walk}
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||||||
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||||||
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@end table
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@customhead{Estimation Options}
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|
@table @code
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@item convergence_starting_value = @var{DOUBLE}
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|
This is the tolerance criterion for convergence and refers to changes in
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|
the objective function value. It should be rather loose since it will
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gradually be tighened during estimation. Default: @code{1e-3}
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@item convergence_ending_value = @var{DOUBLE}
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The convergence criterion ending value. Values much smaller than square
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|
root machine epsilon are probably overkill. Default: @code{1e-6}
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||||||
|
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||||||
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@item convergence_increment_value = @var{DOUBLE}
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||||||
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Determines how quickly the convergence criterion moves from the starting
|
||||||
|
value to the ending value. Default: @code{0.1}
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||||||
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||||||
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@item max_iterations_starting_value = @var{INTEGER}
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||||||
|
This is the maximum number of iterations allowed in the hill-climbing
|
||||||
|
optimization routine and should be rather small since it will gradually
|
||||||
|
be increased during estimation. Default: @code{50}
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||||||
|
|
||||||
|
@item max_iterations_increment_value = @var{DOUBLE}
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||||||
|
Determines how quickly the maximum number of iterations is
|
||||||
|
increased. Default: @code{2}
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||||||
|
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||||||
|
@item max_block_iterations = @var{INTEGER}
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||||||
|
@anchor{max_block_iterations} The parameters are divided into blocks and
|
||||||
|
optimization proceeds over each block. After a set of blockwise
|
||||||
|
optimizations are performed, the convergence criterion is checked and
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||||||
|
the blockwise optimizations are repeated if the criterion is
|
||||||
|
violated. This controls the maximum number of times the blockwise
|
||||||
|
optimization can be performed. Note that after the blockwise
|
||||||
|
optimizations have converged, a single optimization over all the
|
||||||
|
parameters is performed before updating the convergence value and
|
||||||
|
maximum number of iterations. Default: @code{100}
|
||||||
|
|
||||||
|
@item max_repeated_optimization_runs = @var{INTEGER}
|
||||||
|
The entire process described by @ref{max_block_iterations} is repeated
|
||||||
|
until improvement has stopped. This is the maximum number of times the
|
||||||
|
process is allowed to repeat. Set this to @code{0} to not allow
|
||||||
|
repetitions. Default: @code{10}
|
||||||
|
|
||||||
|
@item function_convergence_criterion = @var{DOUBLE}
|
||||||
|
The convergence criterion for the objective function when
|
||||||
|
@code{max_repeated_optimizations_runs} is positive. Default: @code{0.1}
|
||||||
|
|
||||||
|
@item parameter_convergence_criterion = @var{DOUBLE}
|
||||||
|
The convergence criterion for parameter values when
|
||||||
|
@code{max_repeated_optimizations_runs} is positive. Default: @code{0.1}
|
||||||
|
|
||||||
|
@item number_of_large_perturbations = @var{INTEGER}
|
||||||
|
The entire process described by @ref{max_block_iterations} is repeated
|
||||||
|
with random starting values drawn from the posterior. This specifies the
|
||||||
|
number of random starting values used. Set this to @code{0} to not use
|
||||||
|
random starting values. A larger number should be specified to ensure
|
||||||
|
that the entire parameter space has been covererd. Default: @code{5}
|
||||||
|
|
||||||
|
@item number_of_small_perturbations = @var{INTEGER}
|
||||||
|
The number of small perturbations to make after the large perturbations
|
||||||
|
have stopped improving. Setting this number much above @code{10} is
|
||||||
|
probably overkill. Default: @code{5}
|
||||||
|
|
||||||
|
@item number_of_posterior_draws_after_perturbation = @var{INTEGER}
|
||||||
|
The number of consecutive posterior draws to make when producing a small
|
||||||
|
perturbation. Because the posterior draws are serially correlated, a
|
||||||
|
small number will result in a small perturbation. Default: @code{1}
|
||||||
|
|
||||||
|
@item max_number_of_stages = @var{INTEGER}
|
||||||
|
The small and large perturbation are repeated until improvement has
|
||||||
|
stopped. This specifices the maximum number of stages allowed. Default:
|
||||||
|
@code{20}
|
||||||
|
|
||||||
|
@item random_function_convergence_criterion = @var{DOUBLE}
|
||||||
|
The convergence criterion for the objective function when
|
||||||
|
@code{number_of_large_perturbations} is positive. Default: @code{0.1}
|
||||||
|
|
||||||
|
@item random_parameter_convergence_criterion = @var{DOUBLE}
|
||||||
|
The convergence criterion for parameter values when
|
||||||
|
@code{number_of_large_perturbations} is positive. Default: @code{0.1}
|
||||||
|
|
||||||
|
@end table
|
||||||
|
@end deffn
|
||||||
|
|
||||||
|
@examplehead
|
||||||
|
|
||||||
|
@example
|
||||||
|
ms_estimation(datafile=data, initial_year=1959, final_year=2005,
|
||||||
|
nlags=4, max_repeated_optimization_runs=1, max_number_of_stages=0);
|
||||||
|
|
||||||
|
ms_estimation(file_tag=second_run, datafile=data, initial_year=1959,
|
||||||
|
final_year=2005, nlags=4, max_repeated_optimization_runs=1,
|
||||||
|
max_number_of_stages=0);
|
||||||
|
|
||||||
|
ms_estimation(file_tag=second_run, output_file_tag=third_run,
|
||||||
|
no_create_init, max_repeated_optimization_runs=5,
|
||||||
|
number_of_large_perturbations=10);
|
||||||
|
@end example
|
||||||
|
|
||||||
|
|
||||||
|
@anchor{ms_simulation}
|
||||||
|
@deffn Command ms_simulation ;
|
||||||
|
@deffnx Command ms_simulation (@var{OPTIONS}@dots{});
|
||||||
|
@descriptionhead
|
||||||
|
|
||||||
|
Simulates a Markov-switching SBVAR model.
|
||||||
|
|
||||||
|
@optionshead
|
||||||
|
|
||||||
|
@table @code
|
||||||
|
|
||||||
|
@item file_tag = @var{FILENAME}
|
||||||
|
@anchor{file_tag} The portion of the filename associated with the
|
||||||
|
@code{ms_estimation} run. Default: @code{<mod_file>}
|
||||||
|
|
||||||
|
@item output_file_tag = @var{FILENAME}
|
||||||
|
@anchor{output_file_tag} The portion of the output filename that will be
|
||||||
|
assigned to this run. Default: @code{<file_tag>}
|
||||||
|
|
||||||
|
@item mh_replic = @var{INTEGER}
|
||||||
|
The number of draws to save. Default: @code{10,000}
|
||||||
|
|
||||||
|
@item drop = @var{INTEGER}
|
||||||
|
The number of burn-in draws. Default:
|
||||||
|
@code{0.1*mh_replic*thinning_factor}
|
||||||
|
|
||||||
|
@item thinning_factor = @var{INTEGER}
|
||||||
|
The total number of draws is equal to
|
||||||
|
@code{thinning_factor*mh_replic+drop}. Default: @code{1}
|
||||||
|
|
||||||
|
@item adaptive_mh_draws = @var{INTEGER}
|
||||||
|
Tuning period for Metropolis-Hasting draws. Default: @code{30,000}
|
||||||
|
|
||||||
|
@end table
|
||||||
|
@end deffn
|
||||||
|
|
||||||
|
@examplehead
|
||||||
|
|
||||||
|
@example
|
||||||
|
ms_simulation(file_tag=second_run);
|
||||||
|
|
||||||
|
ms_simulation(file_tag=third_run, mh_replic=5000, thinning_factor=3);
|
||||||
|
@end example
|
||||||
|
|
||||||
|
|
||||||
|
@anchor{ms_compute_mdd}
|
||||||
|
@deffn Command ms_compute_mdd ;
|
||||||
|
@deffnx Command ms_compute_mdd (@var{OPTIONS}@dots{});
|
||||||
|
@descriptionhead
|
||||||
|
|
||||||
|
Computes the marginal data density of a Markov-switching SBVAR model
|
||||||
|
from the posterior draws. At the end of the run, the Muller and Bridged
|
||||||
|
log marginal densities are contained in the @code{oo_.ms} structure.
|
||||||
|
|
||||||
|
@optionshead
|
||||||
|
|
||||||
|
@table @code
|
||||||
|
|
||||||
|
@item file_tag = @var{FILENAME}
|
||||||
|
@xref{file_tag}.
|
||||||
|
|
||||||
|
@item output_file_tag = @var{FILENAME}
|
||||||
|
@xref{output_file_tag}.
|
||||||
|
|
||||||
|
@item simulation_file_tag = @var{FILENAME}
|
||||||
|
@anchor{simulation_file_tag} The portion of the filename associated with
|
||||||
|
the simulation run. Defualt: @code{<file_tag>}
|
||||||
|
|
||||||
|
@item proposal_type = @var{INTEGER}
|
||||||
|
The proposal type:
|
||||||
|
@table @code
|
||||||
|
|
||||||
|
@item 1
|
||||||
|
Gaussian
|
||||||
|
|
||||||
|
@item 2
|
||||||
|
Power
|
||||||
|
|
||||||
|
@item 3
|
||||||
|
Truncated Power
|
||||||
|
|
||||||
|
@item 4
|
||||||
|
Step
|
||||||
|
|
||||||
|
@item 5
|
||||||
|
Truncated Gaussian
|
||||||
|
|
||||||
|
@end table
|
||||||
|
|
||||||
|
Default: @code{3}
|
||||||
|
|
||||||
|
@item proposal_lower_bound = @var{DOUBLE}
|
||||||
|
The lower cutoff in terms of probability. Not used for
|
||||||
|
@code{proposal_type} in [@code{1,2}]. Required for all other proposal
|
||||||
|
types. Default: @code{0.1}
|
||||||
|
|
||||||
|
@item proposal_upper_bound = @var{DOUBLE}
|
||||||
|
The upper cutoff in terms of probability. Not used for
|
||||||
|
@code{proposal_type} equal to @code{1}. Required for all other proposal
|
||||||
|
types. Default: @code{0.9}
|
||||||
|
|
||||||
|
@item mdd_proposal_draws = @var{INTEGER}
|
||||||
|
The number of proposal draws. Default: @code{100,000}
|
||||||
|
|
||||||
|
@item mdd_use_mean_center
|
||||||
|
Use the posterior mean as center. Default: @code{off}
|
||||||
|
|
||||||
|
@end table
|
||||||
|
|
||||||
|
@end deffn
|
||||||
|
|
||||||
|
|
||||||
|
@anchor{ms_compute_probabilities}
|
||||||
|
@deffn Command ms_compute_probabilities ;
|
||||||
|
@deffnx Command ms_compute_probabilities (@var{OPTIONS}@dots{});
|
||||||
|
@descriptionhead
|
||||||
|
|
||||||
|
Computes smoothed regime probabilities of a Markov-switching SBVAR
|
||||||
|
model. Output @code{.eps} files are contained in
|
||||||
|
@code{<output_file_tag/Output/Probabilities>}.
|
||||||
|
|
||||||
|
@optionshead
|
||||||
|
|
||||||
|
@table @code
|
||||||
|
|
||||||
|
@item file_tag = @var{FILENAME}
|
||||||
|
@xref{file_tag}.
|
||||||
|
|
||||||
|
@item output_file_tag = @var{FILENAME}
|
||||||
|
@xref{output_file_tag}.
|
||||||
|
|
||||||
|
@item filtered_probabilities
|
||||||
|
Filtered probabilities are computed instead of smoothed. Default:
|
||||||
|
@code{off}
|
||||||
|
|
||||||
|
@item real_time_smoothed
|
||||||
|
Smoothed probabilities are computed based on time @code{t} information
|
||||||
|
for @math{0\le t\le nobs}. Default: @code{off}
|
||||||
|
|
||||||
|
@end table
|
||||||
|
|
||||||
|
@end deffn
|
||||||
|
|
||||||
|
|
||||||
|
@anchor{ms_irf}
|
||||||
|
@deffn Command ms_irf ;
|
||||||
|
@deffnx Command ms_irf (@var{OPTIONS}@dots{});
|
||||||
|
@descriptionhead
|
||||||
|
|
||||||
|
Computes impulse response functions for a Markov-switching SBVAR
|
||||||
|
model. Output @code{.eps} files are contained in
|
||||||
|
@code{<output_file_tag/Output/IRF>}, while data files are contained in
|
||||||
|
@code{<output_file_tag/IRF>}.
|
||||||
|
|
||||||
|
@optionshead
|
||||||
|
|
||||||
|
@table @code
|
||||||
|
|
||||||
|
@item file_tag = @var{FILENAME}
|
||||||
|
@xref{file_tag}.
|
||||||
|
|
||||||
|
@item output_file_tag = @var{FILENAME}
|
||||||
|
@xref{output_file_tag}.
|
||||||
|
|
||||||
|
@item simulation_file_tag = @var{FILENAME}
|
||||||
|
@xref{simulation_file_tag}.
|
||||||
|
|
||||||
|
@item horizon = @var{INTEGER}
|
||||||
|
The forecast horizon. Default: @code{12}
|
||||||
|
|
||||||
|
@item filtered_probabilities
|
||||||
|
@anchor{filtered_probabilities} Uses filtered probabilities at the end
|
||||||
|
of the sample as initial conditions for regime probabilities. Default:
|
||||||
|
@code{off}
|
||||||
|
|
||||||
|
@item no_error_bands
|
||||||
|
@anchor{no_error_bands} Do not output error bands. Default: @code{off}
|
||||||
|
(@i{i.e.} output error bands)
|
||||||
|
|
||||||
|
@item error_band_percentiles = [@var{DOUBLE1} @dots{}]
|
||||||
|
@anchor{error_band_percentiles} The percentiles to compute. Default:
|
||||||
|
@code{[0.16 0.50 0.84]}. If @code{no_error_bands} is passed, the default
|
||||||
|
is @code{[0.5]}
|
||||||
|
|
||||||
|
@item shock_draws = @var{INTEGER}
|
||||||
|
@anchor{shock_draws} The number of regime paths to draw. Default:
|
||||||
|
@code{10,000}
|
||||||
|
|
||||||
|
@item shocks_per_parameter = @var{INTEGER}
|
||||||
|
@anchor{shocks_per_parameter} The number of regime paths to draw under
|
||||||
|
parameter uncertainty. Default: @code{10}
|
||||||
|
|
||||||
|
@item thinning_factor = @var{INTEGER}
|
||||||
|
@anchor{thinning_factor} Only @math{1/@code{thinning_factor}} of the
|
||||||
|
draws in posterior draws file are used. Default: @code{1}
|
||||||
|
|
||||||
|
@item free_parameters = @var{NUMERICAL_VECTOR}
|
||||||
|
@anchor{free_parameters} A vector of free parameters to initialize theta
|
||||||
|
of the model. Default: use estimated parameters
|
||||||
|
|
||||||
|
@item median
|
||||||
|
@anchor{median}
|
||||||
|
|
||||||
|
A shortcut to setting @code{error_band_percentiles=[0.5]}. Default:
|
||||||
|
@code{off}
|
||||||
|
|
||||||
|
@end table
|
||||||
|
|
||||||
|
@end deffn
|
||||||
|
|
||||||
|
|
||||||
|
@anchor{ms_forecast}
|
||||||
|
@deffn Command ms_forecast ;
|
||||||
|
@deffnx Command ms_forecast (@var{OPTIONS}@dots{});
|
||||||
|
@descriptionhead
|
||||||
|
|
||||||
|
Generates forecasts for a Markov-switching SBVAR model. Output
|
||||||
|
@code{.eps} files are contained in @code{<output_file_tag/Output/IRF>},
|
||||||
|
while data files are contained in @code{<output_file_tag/IRF>}.
|
||||||
|
|
||||||
|
@optionshead
|
||||||
|
|
||||||
|
@table @code
|
||||||
|
|
||||||
|
@item file_tag = @var{FILENAME}
|
||||||
|
@xref{file_tag}.
|
||||||
|
|
||||||
|
@item output_file_tag = @var{FILENAME}
|
||||||
|
@xref{output_file_tag}.
|
||||||
|
|
||||||
|
@item simulation_file_tag = @var{FILENAME}
|
||||||
|
@xref{simulation_file_tag}.
|
||||||
|
|
||||||
|
@item data_obs_nbr = @var{INTEGER}
|
||||||
|
The number of data points included in the output. Default: @code{0}
|
||||||
|
|
||||||
|
@item no_error_bands
|
||||||
|
@xref{no_error_bands}.
|
||||||
|
|
||||||
|
@item error_band_percentiles = [@var{DOUBLE1} @dots{}]
|
||||||
|
@xref{error_band_percentiles}.
|
||||||
|
|
||||||
|
@item shock_draws = @var{INTEGER}
|
||||||
|
@xref{shock_draws}.
|
||||||
|
|
||||||
|
@item shocks_per_parameter = @var{INTEGER}
|
||||||
|
@xref{shocks_per_parameter}.
|
||||||
|
|
||||||
|
@item thinning_factor = @var{INTEGER}
|
||||||
|
@xref{thinning_factor}.
|
||||||
|
|
||||||
|
@item free_parameters = @var{NUMERICAL_VECTOR}
|
||||||
|
@xref{free_parameters}.
|
||||||
|
|
||||||
|
@item median
|
||||||
|
|
||||||
|
@xref{median}.
|
||||||
|
|
||||||
|
@end table
|
||||||
|
|
||||||
|
@end deffn
|
||||||
|
|
||||||
|
|
||||||
|
@anchor{ms_variance_decomposition}
|
||||||
|
@deffn Command ms_variance_decomposition ;
|
||||||
|
@deffnx Command ms_variance_decomposition (@var{OPTIONS}@dots{});
|
||||||
|
@descriptionhead
|
||||||
|
|
||||||
|
Computes the variance decomposition for a Markov-switching SBVAR
|
||||||
|
model. Output @code{.eps} files are contained in
|
||||||
|
@code{<output_file_tag/Output/Variance_Decomposition>}, while data files
|
||||||
|
are contained in @code{<output_file_tag/Variance_Decomposition>}.
|
||||||
|
|
||||||
|
@optionshead
|
||||||
|
|
||||||
|
@table @code
|
||||||
|
|
||||||
|
@item file_tag = @var{FILENAME}
|
||||||
|
@xref{file_tag}.
|
||||||
|
|
||||||
|
@item output_file_tag = @var{FILENAME}
|
||||||
|
@xref{output_file_tag}.
|
||||||
|
|
||||||
|
@item simulation_file_tag = @var{FILENAME}
|
||||||
|
@xref{simulation_file_tag}.
|
||||||
|
|
||||||
|
@item filtered_probabilities
|
||||||
|
@xref{filtered_probabilities}.
|
||||||
|
|
||||||
|
@item no_error_bands
|
||||||
|
@xref{no_error_bands}.
|
||||||
|
|
||||||
|
@item error_band_percentiles = [@var{DOUBLE1} @dots{}]
|
||||||
|
@xref{error_band_percentiles}.
|
||||||
|
|
||||||
|
@item shock_draws = @var{INTEGER}
|
||||||
|
@xref{shock_draws}.
|
||||||
|
|
||||||
|
@item shocks_per_parameter = @var{INTEGER}
|
||||||
|
@xref{shocks_per_parameter}.
|
||||||
|
|
||||||
|
@item thinning_factor = @var{INTEGER}
|
||||||
|
@xref{thinning_factor}.
|
||||||
|
|
||||||
|
@item free_parameters = @var{NUMERICAL_VECTOR}
|
||||||
|
@xref{free_parameters}.
|
||||||
|
|
||||||
|
@item median
|
||||||
|
|
||||||
|
@xref{median}.
|
||||||
|
|
||||||
|
@end table
|
||||||
|
|
||||||
|
@end deffn
|
||||||
|
|
||||||
|
|
||||||
@node Displaying and saving results
|
@node Displaying and saving results
|
||||||
@section Displaying and saving results
|
@section Displaying and saving results
|
||||||
|
|
||||||
|
@ -6314,6 +6947,11 @@ General Equilibrium Models Using a Second-Order Approximation to the
|
||||||
Policy Function,'' @i{Journal of Economic Dynamics and Control},
|
Policy Function,'' @i{Journal of Economic Dynamics and Control},
|
||||||
28(4), 755--775.
|
28(4), 755--775.
|
||||||
|
|
||||||
|
@item
|
||||||
|
Sims, Christopher A., Daniel F. Waggoner and Tao Zha (2008): ``Methods for
|
||||||
|
inference in large multiple-equation Markov-switching models,''
|
||||||
|
@i{Journal of Econometrics}, 146, 255--274.
|
||||||
|
|
||||||
@item
|
@item
|
||||||
Smets, Frank and Rafael Wouters (2003): ``An Estimated Dynamic
|
Smets, Frank and Rafael Wouters (2003): ``An Estimated Dynamic
|
||||||
Stochastic General Equilibrium Model of the Euro Area,'' @i{Journal of
|
Stochastic General Equilibrium Model of the Euro Area,'' @i{Journal of
|
||||||
|
|
|
@ -1 +1 @@
|
||||||
Subproject commit 1a61d4ad39f81193babf4a7f09deefd65ffbcdfe
|
Subproject commit fc3ee3d8f4268c44150358fa166dc97eab1c9b26
|
Loading…
Reference in New Issue