Add routine for dynamic contributions in semi-structural models.
parent
5e869669a0
commit
bd0493d135
GPG Key ID:
A6D44CB9C64CE77B
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function dcontrib(varargin)
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% Computes dynamic contributions to a subset of endogenous variables in a semi structural model.
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%
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% EXAMPLE
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%
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% >> dcontrib --model sandbox.mod --tags zpac eq:x1 --database ds --output results --range 2023Q1:2073Q1
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%
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% zpac and eq:x1 are the equation tags of the equations determining the endogenous variables for which we want to compute
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% the contributions of the other (exogenous) variables, sandbox.mod is the name of the file from which we exctract these
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% equations, ds is a dseries object containing the data, 2023Q1:2073Q1 is the time range over which we compute the
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% contributions, and results the name of the structure containing the contributions (as dseries objects) for each endogenous
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% variable.
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%
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% INPUTS
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% --model name of a mod file (with extension)
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% --tags list of equations (equation tags assocated to the endogenous variables for which we want to compute the contributions)
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% --database dseries object
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% --baseline dseries object (path for the exogenous variables)
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% --range followed by a dates range
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%
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% REMARKS
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% [1] --baseline and --range are not compatible.
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% [2] --variables is followed by a space separated list of names, it is assumed that each variable is associated with an equation tag.
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% Copyright © 2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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global M_
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if nargin==1 && strcmpi(varargin{1}, '--help')
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skipline()
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disp('--model followed by the name of a mod file (with extension) [mandatory]')
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disp('--tags followed by a list of equation tags [mandatory]')
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disp('--database followed by dseries object [mandatory]')
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disp('--baseline followed by dseries object (path for the exogenous variables)')
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disp('--range followed by a dates range')
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disp('--output followed by a name for the structure holding the results [mandatory]')
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skipline()
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return
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end
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model = getmodel(varargin);
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% First call to dynare to obtain the json verison of the model.
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dynare(model(1:end-4), 'nopreprocessoroutput', 'notime', 'json=compute')
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delete(sprintf('%s.log', model(1:end-4)))
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eqtags = geteqtags(varargin);
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variables = cell(length(eqtags), 1);
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for i=1:length(eqtags)
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variables(i) = get_variables_and_parameters_in_expression(get_lhs_and_rhs(eqtags{i}, M_, true));
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end
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% Cherry pick equations required for the decomposition.
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cherrypickdir = sprintf('cherry-pick-%s', randomstring(10));
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cherrypick(model(1:end-4), cherrypickdir, eqtags, false);
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rmdir(model(1:end-4), 's')
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rmdir(sprintf('+%s', model(1:end-4)), 's')
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modfilename = sprintf('dcontrib_%s.mod', randomstring(10));
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aggregate(modfilename, {}, '', cherrypickdir);
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rmdir(cherrypickdir, 's')
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% Second call to dynare (on the exctracted equations)
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dynare(modfilename(1:end-4), 'nopreprocessoroutput', 'notime', 'json=compute')
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% Get dataset
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dname = getdatasetname(varargin);
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ds = evalin('caller', dname);
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if ~isdseries(ds)
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error('dcontrib:getdataset: --dataset must be followed by a dseries object.')
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end
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% Create a dseries object for the paths of the exogenous variables
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xvariables = ds{M_.exo_names{:}};
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% Get initial and terminal periods (if defined)
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[firstperiod, lastperiod] = getperiods(varargin);
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if firstperiod<=ds.dates(1)+M_.orig_maximum_lag
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error('dcontrib:: Try increase firstperiod (>%s).', char(ds.dates(1)+M_.orig_maximum_lag))
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end
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if lastperiod>ds.dates(end)
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error('dcontrib:: Try reduce lastperiod (<=%s).', char(ds.dates(end)))
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end
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% Load baseline (if it makes sense)
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if isempty(firstperiod)
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baselinename = getbaselinename(varargin);
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baseline = evalin('caller', baselinename);
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if ~isdseries(baseline)
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error('dcontrib:getdataset: --baseline must be followed by a dseries object.')
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end
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firstperiod = baseline.dates(1);
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lastperiod = baseline.dates(end);
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baseline = baseline{M_.exo_names{:}};
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else
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% Set default baseline (exogenous variable levels in firstperiod-1)
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baseline = xvariables(firstperiod-1);
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baseline = repmat(baseline.data, lastperiod-firstperiod+1, 1);
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baseline = dseries(baseline, firstperiod, M_.exo_names);
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end
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% Restrict the observations for the exogenous variables to the pertinent tim range
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xvariables = xvariables(firstperiod:lastperiod);
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% Set initial conditions for the simulation.
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initialconditions = ds(ds.dates(1):firstperiod-1);
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% Simulation on the baseline (track the effects of the initial state if the model is autoregressive)
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S.baseline = simul_backward_model(initialconditions, lastperiod-firstperiod+1, baseline);
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% contributions is a dseries object holding the marginal contribution of the baseline and
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% each exogenous variable to endogenous variable z
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% contributions.baseline = S.baseline(firstperiod:lastperiod);
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% Add exogenous variables one by one and simulate the model (-> cumulated contributions)
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for i=1:xvariables.vobs
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name = xvariables.name{i};
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baseline{name} = xvariables{name};
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S.(name) = simul_backward_model(initialconditions, lastperiod-firstperiod+1, baseline);
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end
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% Compute marginal contributions
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for j=1:length(variables)
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cumulatedcontribs = S.baseline{variables{j}}(firstperiod:lastperiod).data;
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contributions.(variables{j}) = dseries(cumulatedcontribs, firstperiod, 'baseline');
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for i=1:xvariables.vobs
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name = xvariables.name{i};
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ts = S.(name);
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data = ts{variables{j}}(firstperiod:lastperiod).data;
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contributions.(variables{j}) = [contributions.(variables{j}), dseries(data-cumulatedcontribs, firstperiod, name)];
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cumulatedcontribs = data;
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end
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contributions.(variables{j}) = contributions.(variables{j})(firstperiod:lastperiod);
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end
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% Save output in caller workspace
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oname = getoutputname(varargin);
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assignin('caller', oname, contributions)
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% Cleanup
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rmdir(modfilename(1:end-4), 's')
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rmdir(sprintf('+%s', modfilename(1:end-4)), 's')
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delete(sprintf('%s.mod', modfilename(1:end-4)))
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delete(sprintf('%s.log', modfilename(1:end-4)))
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end
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function model = getmodel(cellarray)
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% Return variables for which we want to compute the contributions.
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%
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% INPUTS
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% - cellarray [char] 1×n cell array of row char arrays.
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%
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% OUTPUTS
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% - var [char] name of the model (with extension)
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mpos = positions(cellarray);
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model = cellarray{mpos+1};
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end
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function eqtags = geteqtags(cellarray)
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% Return equation tags for the equations we want to compute the contributions.
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%
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% INPUTS
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% - cellarray [char] 1×n cell array of row char arrays.
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%
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% OUTPUTS
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% - eqtags [char] 1×p cell array of row char arrays.
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[~, vpos, ~, ~, ~, ~, indices] = positions(cellarray);
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lastvalue = indices(find(indices==vpos)+1)-1;
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eqtags = cellarray(vpos+1:lastvalue);
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end
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function dname = getdatasetname(cellarray)
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% Return the name of the dataset.
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%
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% INPUTS
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% - cellarray [char] 1×n cell array of row char arrays.
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%
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% OUTPUTS
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% - dname [char] dataset name for endogenous and exogenous variables
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[~, ~, dpos] = positions(cellarray);
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dname = cellarray{dpos+1};
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end
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function [firstperiod, lastperiod] = getperiods(cellarray)
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% Return variables for which we want to compute the contributions.
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%
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% INPUTS
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% - cellarray [char] 1×n cell array of row char arrays.
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%
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% OUTPUTS
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% - ds [dseries] dataset for endogenous and exogenous variables
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[~, ~, ~, rpos] = positions(cellarray);
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firstperiod = dates();
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lastperiod = dates();
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if ~isempty(rpos)
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try
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tmp = strsplit(cellarray{rpos+1},':');
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firstperiod = dates(tmp{1});
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lastperiod = dates(tmp{2});
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catch
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error('dcontrib:getperiods: Cannot convert the --range argument to dates objects.')
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end
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if lastperiod<=firstperiod
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error('dcontrib:getperiods: In --range A:B we must have B>A.')
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end
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end
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end
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function dname = getbaselinename(cellarray)
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% Return the name of the dataset.
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%
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% INPUTS
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% - cellarray [char] 1×n cell array of row char arrays.
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%
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% OUTPUTS
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% - dname [char] baseline name for endogenous and exogenous variables
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[~, ~, ~, ~, bpos] = positions(cellarray);
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dname = cellarray{bpos+1};
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end
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function oname = getoutputname(cellarray)
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% Return the name of the output.
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%
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% INPUTS
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% - cellarray [char] 1×n cell array of row char arrays.
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%
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% OUTPUTS
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% - dname [char] baseline name for endogenous and exogenous variables
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[~, ~, ~, ~, ~, opos] = positions(cellarray);
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oname = cellarray{opos+1};
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end
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function [mpos, vpos, dpos, rpos, bpos, opos, indices] = positions(cellarray)
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% Return positions of the arguments.
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%
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% INPUTS
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% - cellarray [char] 1×n cell array of row char arrays.
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%
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% OUTPUTS
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% - mpos [integer] scalar, index for the --model argument.
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% - vpos [integer] scalar, index for the --tags arguments.
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% - dpos [integer] scalar, index for the --database argument.
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% - rpos [integer] scalar, index for the --range argument.
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% - bpos [integer] scalar. index for the --baseline argument.
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% - opos [integer] scalar, index for the --output argument.
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% Index for --model argument
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mpos = find(strcmp('--model', cellarray));
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if isempty(mpos)
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error('dcontrib::positions: --model argument is mandatory.')
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elseif length(mpos)>1
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error('dplot::positions: Only one --model argument is allowed.')
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end
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% Index for --tags argument
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vpos = find(strcmp('--tags', cellarray));
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if isempty(vpos)
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error('dplot::positions: --tags argument is mandatory.')
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elseif length(vpos)>1
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error('dplot::positions: Only one --tags argument is allowed.')
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end
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% Index for the --initialconditions argument
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dpos = find(strcmp('--database', cellarray));
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if isempty(dpos)
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error('dplot::positions: --database argument is mandatory.')
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elseif length(dpos)>1
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error('dplot::positions: Only one --database argument is allowed.')
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end
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% Index for the --range argument
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rpos = find(strcmp('--range', cellarray));
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if length(rpos)>1
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error('dplot::positions: Only one --range argument is allowed.')
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end
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% Index for the --baseline argument
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bpos = find(strcmp('--baseline', cellarray));
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if length(bpos)>1
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error('dplot::positions: Only one --baseline argument is allowed.')
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end
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if ~isempty(rpos) && ~isempty(bpos)
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error('dplot::positions: --baseline and --range arguments are not allowed simultaneously.')
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end
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% Index for the --output argument.
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opos = find(strcmp('--output', cellarray));
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if isempty(opos)
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error('dplot::positions: --output argument is mandatory.')
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elseif length(opos)>1
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error('dplot::positions: Only one --periods argument is allowed.')
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end
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% Sorted vector of indices
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indices = sort([mpos; vpos; dpos; rpos; bpos; opos]);
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end
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@ -1269,7 +1269,8 @@ M_TRS_FILES += run_block_byte_tests_matlab.m.trs \
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nonlinearsolvers.m.trs \
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cyclereduction.m.trs \
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logarithmicreduction.m.trs \
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riccatiupdate.m.trs
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riccatiupdate.m.trs \
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contribs.m.trs
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M_XFAIL_TRS_FILES = $(patsubst %.mod, %.m.trs, $(XFAIL_MODFILES))
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@ -1284,7 +1285,8 @@ O_TRS_FILES += run_block_byte_tests_octave.o.trs \
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nonlinearsolvers.o.trs \
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cyclereduction.o.trs \
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logarithmicreduction.o.trs \
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riccatiupdate.o.trs
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riccatiupdate.o.trs \
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contribs.o.trs
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O_XFAIL_TRS_FILES = $(patsubst %.mod, %.o.trs, $(XFAIL_MODFILES))
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@ -1491,7 +1493,10 @@ EXTRA_DIST = \
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solve_algo_12_14/simul_backward_common.inc \
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solve_algo_12_14/purely_backward_common.inc \
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solve_algo_12_14/purely_static_common.inc \
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solve_algo_12_14/purely_forward_common.inc
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solve_algo_12_14/purely_forward_common.inc \
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sandbox.mod \
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simulateddata.m
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if ENABLE_MATLAB
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check-local: check-matlab
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@ -0,0 +1,82 @@
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debug = false;
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if debug
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[top_test_dir, ~, ~] = fileparts(mfilename('fullpath'));
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else
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top_test_dir = getenv('TOP_TEST_DIR');
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end
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addpath(sprintf('%s/matlab', top_test_dir(1:end-6)))
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if ~debug
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% Test Dynare Version
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if ~strcmp(dynare_version(), getenv('DYNARE_VERSION'))
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error('Incorrect version of Dynare is being tested')
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end
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end
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dynare_config;
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NumberOfTests = 0;
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testFailed = 0;
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if ~debug
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skipline()
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disp('*** TESTING: nonlinearsolvers.m ***');
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end
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%
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% TEST
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%
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t0 = clock;
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NumberOfTests = NumberOfTests+1;
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try
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dataset = dseries('simulateddata.m');
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dcontrib --model sandbox.mod --tags zpac eq:x1 --database dataset --output results --range 2023Q1:2073Q1
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if max(abs(sum(results.z.data, 2)-dataset.z(dates('2023Q1'):dates('2073Q1')).data))>1e-5
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error('Computation of dynamic contributions failed.')
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end
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catch
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testFailed = testFailed+1;
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end
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t1 = clock;
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if ~debug
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cd(getenv('TOP_TEST_DIR'));
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else
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dprintf('FAILED tests: %i', testFailed)
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end
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if isoctave
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fid = fopen('contribs.o.trs', 'w+');
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else
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fid = fopen('contribs.m.trs', 'w+');
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end
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if testFailed
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fprintf(fid,':test-result: FAIL\n');
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fprintf(fid,':list-of-failed-tests: nonlinearsolvers.m\n');
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else
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fprintf(fid,':test-result: PASS\n');
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end
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fprintf(fid,':number-tests: %i\n', NumberOfTests);
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fprintf(fid,':number-failed-tests: %i\n', testFailed);
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fprintf(fid,':elapsed-time: %f\n', etime(t1, t0));
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fclose(fid);
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if ~debug
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exit;
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end
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%
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% END OF TEST
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%
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@ -0,0 +1,134 @@
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// --+ options: json=compute, stochastic +--
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@#define simulate = false
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var x1 x2 x1bar x2bar z y x u v s azertyuiopiop z1 z2 z3;
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varexo ex1
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ex2
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ex1bar
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ex2bar
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ez
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ey
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ex
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eu
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ev
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es;
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parameters
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rho_1 rho_2 rho_3 rho_4
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a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
|
||||
a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
|
||||
e_c_m c_z_1 c_z_2 c_z_dx2 c_z_u c_z_dv c_z_s cx cy beta
|
||||
lambda;
|
||||
|
||||
rho_1 = .9;
|
||||
rho_2 = -.2;
|
||||
rho_3 = .4;
|
||||
rho_4 = -.3;
|
||||
|
||||
|
||||
a_x1_0 = -.9;
|
||||
a_x1_1 = .4;
|
||||
a_x1_2 = .3;
|
||||
a_x1_x2_1 = .1;
|
||||
a_x1_x2_2 = .2;
|
||||
|
||||
a_x2_0 = -.9;
|
||||
a_x2_1 = .2;
|
||||
a_x2_2 = -.1;
|
||||
a_x2_x1_1 = -.1;
|
||||
a_x2_x1_2 = .2;
|
||||
|
||||
beta = .2;
|
||||
e_c_m = .5;
|
||||
c_z_1 = .2;
|
||||
c_z_2 = -.1;
|
||||
c_z_dx2 = .3;
|
||||
c_z_u = .3;
|
||||
c_z_dv = .4;
|
||||
c_z_s = -.2;
|
||||
cx = 1.0;
|
||||
cy = 1.0;
|
||||
|
||||
|
||||
lambda = 0.5; // Share of optimizing agents.
|
||||
|
||||
trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
|
||||
|
||||
pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman, auxname=rototo);
|
||||
|
||||
model;
|
||||
|
||||
[name='eq:u']
|
||||
s = .3*s(-1) - .1*s(-2) + es;
|
||||
|
||||
[name='eq:diff(v)']
|
||||
diff(v) = .5*diff(v(-1)) + ev;
|
||||
|
||||
[name='eq:u']
|
||||
u = .5*u(-1) - .2*u(-2) + eu;
|
||||
|
||||
[name='eq:y']
|
||||
y = rho_1*y(-1) + rho_2*y(-2) + ey;
|
||||
|
||||
[name='eq:x']
|
||||
x = rho_3*x(-1) + rho_4*x(-2) + ex;
|
||||
|
||||
[name='eq:azertyuiopiop', rename='azertyuiopiop->qsdfghjklm']
|
||||
azertyuiopiop = x + y;
|
||||
|
||||
[name='eq:x1']
|
||||
diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
|
||||
|
||||
[name='eq:x2']
|
||||
diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
|
||||
|
||||
[name='eq:x1bar']
|
||||
x1bar = x1bar(-1) + ex1bar;
|
||||
|
||||
[name='eq:x2bar']
|
||||
x2bar = x2bar(-1) + ex2bar;
|
||||
|
||||
[name='zpac']
|
||||
diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + c_z_s*s + c_z_dv*diff(v) ) + (1-lambda)*( cy*y + cx*x) + c_z_u*u + c_z_dx2*diff(x2) + ez;
|
||||
|
||||
[name='z1']
|
||||
z1 = z+y-x+u;
|
||||
|
||||
[name='z2']
|
||||
z2 = z-y+x-u;
|
||||
|
||||
[name='z3']
|
||||
z3 = u-diff(v);
|
||||
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var ex1 = 1.0;
|
||||
var ex2 = 1.0;
|
||||
var ex1bar = 1.0;
|
||||
var ex2bar = 1.0;
|
||||
var ez = 1.0;
|
||||
var ey = 0.1;
|
||||
var ex = 0.1;
|
||||
var eu = 0.05;
|
||||
var ev = 0.05;
|
||||
var es = 0.07;
|
||||
end;
|
||||
|
||||
// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
|
||||
pac.initialize('pacman');
|
||||
|
||||
// Update the parameters of the PAC expectation model (h0 and h1 vectors).
|
||||
pac.update.expectation('pacman');
|
||||
|
||||
@#if simulate
|
||||
verbatim;
|
||||
// Simulate the model to create an artificial sample.
|
||||
initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
|
||||
TrueData = simul_backward_model(initialconditions, 300);
|
||||
TrueData.save('simulateddata', 'm')
|
||||
end;
|
||||
|
||||
@#endif
|
||||
File diff suppressed because it is too large
Load Diff
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Reference in New Issue