Added the possibility to use linear expressions in VAR_EXPECTATION_MODEL.
See tests/var-expectations/9/example.mod for a self documented example. Also updated all the integration tests using the option `expression` instead of `variable` (which is deprecated and will be removed at some point).time-shift
Stéphane Adjemia (Scylla)
parent
650c78f99d
commit
53ab321de8
GPG Key ID:
A6D44CB9C64CE77B
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@ -84,8 +84,17 @@ if discountfactor>1
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error('The discount cannot be greater than one.')
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end
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% Set variable_id in VAR model
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variable_id_in_var = strcmp(auxmodel.list_of_variables_in_companion_var, varexpectationmodel.variable);
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% Set variables_id in VAR model
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m = length(varexpectationmodel.expr.vars);
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variables_id_in_var = NaN(m,1);
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for i = 1:m
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j = find(strcmp(auxmodel.list_of_variables_in_companion_var, DynareModel.endo_names{varexpectationmodel.expr.vars(i)}));
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if isempty(j)
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error('Cannot find variable %s in the companion VAR', DynareModel.endo_names{varexpectationmodel.expr.vars(i)})
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else
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variables_id_in_var(i) = find(strcmp(auxmodel.list_of_variables_in_companion_var, DynareModel.endo_names{varexpectationmodel.expr.vars(i)}));
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end
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end
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% Get the horizon parameter.
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horizon = varexpectationmodel.horizon;
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@ -127,8 +136,10 @@ CompanionMatrix = auxcalib.CompanionMatrix;
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n = length(CompanionMatrix);
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% Set the selection vector
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alpha = zeros(1, length(CompanionMatrix));
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alpha(variable_id_in_var) = 1;
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alpha = zeros(1, n);
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alpha(variables_id_in_var) = varexpectationmodel.expr.constants;
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params_id_in_var = ~isnan(varexpectationmodel.expr.params);
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alpha(variables_id_in_var(params_id_in_var)) = varexpectationmodel.expr.params(params_id_in_var);
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if length(horizon)==1
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% Compute the reduced form parameters of the (discounted) forecast in period t+horizon(1)
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@ -368,6 +368,7 @@ MODFILES = \
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var-expectations/7/substitution.mod \
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var-expectations/8/example.mod \
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var-expectations/8/substitution.mod \
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var-expectations/9/example.mod \
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trend-component-and-var-models/vm1.mod \
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trend-component-and-var-models/vm2.mod \
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trend-component-and-var-models/vm3.mod \
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@ -36,7 +36,7 @@ var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
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*/
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 1, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = x, auxiliary_model_name = toto, horizon = 1, discount = beta) ;
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model;
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@ -36,7 +36,7 @@ var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
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*/
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 2, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = x, auxiliary_model_name = toto, horizon = 2, discount = beta) ;
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model;
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@ -36,7 +36,7 @@ var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
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*/
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 0:Inf, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = x, auxiliary_model_name = toto, horizon = 0:Inf, discount = beta) ;
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model;
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@ -36,7 +36,7 @@ var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
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*/
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 0:500, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = x, auxiliary_model_name = toto, horizon = 0:500, discount = beta) ;
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model;
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@ -36,7 +36,7 @@ var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
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*/
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 15:50, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = x, auxiliary_model_name = toto, horizon = 15:50, discount = beta) ;
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model;
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[ name = 'X' ]
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@ -47,7 +47,7 @@ trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq
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** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
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*/
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var_expectation_model(model_name = varexp, variable = x1, auxiliary_model_name = toto, horizon = 15:50, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = x1, auxiliary_model_name = toto, horizon = 15:50, discount = beta) ;
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model;
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@ -81,14 +81,29 @@ end
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var_expectation.print('varexp');
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shocks;
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var ex1 = .01;
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var ex2 = .01;
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var ex1bar = .02;
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var ex2bar = .02;
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end;
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = example.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
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ynames = M_.endo_names;
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save('example.mat', 'residual', 'g1');
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initialconditions =zeros(3,5);
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initialconditions(3,1) = .10; % foo(-1)
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initialconditions(3,2) = .20; % x1(-1)
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initialconditions(2,2) = .22; % x1(-2)
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initialconditions(1,2) = .24; % x1(-3)
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initialconditions(3,3) = .30; % x2(-1)
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initialconditions(2,3) = .32; % x2(-2)
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initialconditions(1,3) = .34; % x2(-3)
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initialconditions(3,4) = .25; % x1bar(-1)
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initialconditions(3,5) = .25; % x2bar(-1)
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initialconditions = ...
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dseries(initialconditions, dates('2000Q1'), {'foo', 'x1','x2', 'x1bar', 'x2bar'});
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set_dynare_seed('default');
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ts = simul_backward_model(initialconditions, 100);
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foo = ts.foo.data;
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save('example.mat', 'foo');
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end;
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@ -45,23 +45,32 @@ foo = .5*foo(-1) +
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;
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end;
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = substitution.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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example = load('example.mat');
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[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
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shocks;
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var ex1 = .01;
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var ex2 = .01;
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var ex1bar = .02;
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var ex2bar = .02;
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end;
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if max(abs(example.residual-residual))>1e-8
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error('Residuals do not match!')
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end
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if max(max(abs(example.g1-g1)))>1e-8
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error('Jacobian matrices do not match!')
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end
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delete('example.mat');
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verbatim;
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initialconditions =zeros(3,5);
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initialconditions(3,1) = .10; % foo(-1)
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initialconditions(3,2) = .20; % x1(-1)
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initialconditions(2,2) = .22; % x1(-2)
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initialconditions(1,2) = .24; % x1(-3)
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initialconditions(3,3) = .30; % x2(-1)
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initialconditions(2,3) = .32; % x2(-2)
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initialconditions(1,3) = .34; % x2(-3)
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initialconditions(3,4) = .25; % x1bar(-1)
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initialconditions(3,5) = .25; % x2bar(-1)
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initialconditions = ...
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dseries(initialconditions, dates('2000Q1'), {'foo', 'x1','x2', 'x1bar', 'x2bar'});
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set_dynare_seed('default');
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ts = simul_backward_model(initialconditions, 100);
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ex = load('example.mat');
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delete('example.mat')
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if max(abs(ex.foo-ts.foo.data))>1e-12
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error('Simulations do not match!')
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end
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end;
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@ -17,7 +17,7 @@ beta = 1/(1+.02);
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var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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// Define a VAR_EXPECTATION_MODEL
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 1, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = diff(x), auxiliary_model_name = toto, horizon = 1, discount = beta) ;
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model;
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@ -40,13 +40,26 @@ var_expectation.update('varexp');
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// Print expanded VAR_EXPECTATION expression in a file (to be included in substitution.mod).
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var_expectation.print('varexp');
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = example.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
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save('example.mat', 'residual', 'g1');
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shocks;
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var e_x = .01;
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var e_y = .01;
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var e_z = .01;
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end;
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verbatim;
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initialconditions =zeros(3,4);
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initialconditions(3,1) = .1; % foo(-1)
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initialconditions(:,2) = .2; % y(-1)
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initialconditions(3,3) = .3; % z(-1)
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initialconditions(2,3) = .4; % z(-2)
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initialconditions(3,4) = .5; % x(-1)
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initialconditions(2,4) = .6; % x(-2)
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initialconditions(1,4) = .7; % x(-3)
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initialconditions = ...
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dseries(initialconditions, dates('2000Q1'), {'foo', 'y','z', 'x'});
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set_dynare_seed('default');
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ts = simul_backward_model(initialconditions, 100);
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foo = ts.foo.data;
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save('example.mat', 'foo');
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end;
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@ -31,23 +31,31 @@ foo = .5*foo(-1) +
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;
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end;
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = substitution.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
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example = load('example.mat');
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shocks;
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var e_x = .01;
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var e_y = .01;
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var e_z = .01;
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end;
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if max(abs(example.residual-residual))>1e-8
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error('Residuals do not match!')
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verbatim;
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initialconditions =zeros(3,4);
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initialconditions(3,1) = .1; % foo(-1)
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initialconditions(:,2) = .2; % y(-1)
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initialconditions(3,3) = .3; % z(-1)
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initialconditions(2,3) = .4; % z(-2)
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initialconditions(3,4) = .5; % x(-1)
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initialconditions(2,4) = .6; % x(-2)
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initialconditions(1,4) = .7; % x(-3)
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initialconditions = ...
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dseries(initialconditions, dates('2000Q1'), {'foo', 'y','z', 'x'});
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set_dynare_seed('default');
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ts = simul_backward_model(initialconditions, 100);
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ex = load('example.mat');
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end;
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delete('example.mat')
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if max(abs(ex.foo-ts.foo.data))>1e-12
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error('Simulations do not match!')
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end
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if max(max(abs(example.g1-g1)))>1e-8
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error('Jacobian matrices do not match!')
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end
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delete('example.mat')
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@ -17,7 +17,7 @@ beta = 1/(1+.02);
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var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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// Define a VAR_EXPECTATION_MODEL
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 1, discount = beta) ;
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var_expectation_model(model_name = varexp, expression = diff(log(x)), auxiliary_model_name = toto, horizon = 1, discount = beta) ;
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model;
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[ name = 'X' ]
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@ -30,22 +30,34 @@ log(y) = d*log(y(-2)) + e*diff(z(-1)) + e_y;
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foo = .5*foo(-1) + var_expectation(varexp);
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end;
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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// Print expanded VAR_EXPECTATION expression in a file (to be included in substitution.mod).
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var_expectation.print('varexp');
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shocks;
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var e_x = .01;
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var e_y = .01;
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var e_z = .01;
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end;
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = example.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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[residual, g1] = example.dynamic(y, x', M_.params, oo_.steady_state, 1);
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save('example.mat', 'residual', 'g1');
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initialconditions =zeros(3,4);
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initialconditions(3,1) = .1; % foo(-1)
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initialconditions(:,2) = .2; % y(-1)
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initialconditions(3,3) = .3; % z(-1)
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initialconditions(2,3) = .4; % z(-2)
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initialconditions(3,4) = .5; % x(-1)
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initialconditions(2,4) = .6; % x(-2)
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initialconditions(1,4) = .7; % x(-3)
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initialconditions = ...
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dseries(initialconditions, dates('2000Q1'), {'foo', 'y','z', 'x'});
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set_dynare_seed('default');
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ts = simul_backward_model(initialconditions, 100);
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foo = ts.foo.data;
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save('example.mat', 'foo');
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end;
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@ -28,23 +28,30 @@ foo = .5*foo(-1) +
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;
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end;
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verbatim;
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set_dynare_seed('default');
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y = zeros(M_.endo_nbr,1);
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y(1:M_.orig_endo_nbr) = rand(M_.orig_endo_nbr, 1);
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x = randn(M_.exo_nbr,1);
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y = substitution.set_auxiliary_variables(y, x, M_.params);
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y = [y(find(M_.lead_lag_incidence(1,:))); y];
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[residual, g1] = substitution.dynamic(y, x', M_.params, oo_.steady_state, 1);
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example = load('example.mat');
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shocks;
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var e_x = .01;
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var e_y = .01;
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var e_z = .01;
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end;
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if max(abs(example.residual-residual))>1e-8
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error('Residuals do not match!')
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end
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verbatim;
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initialconditions =zeros(3,4);
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initialconditions(3,1) = .1; % foo(-1)
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initialconditions(:,2) = .2; % y(-1)
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initialconditions(3,3) = .3; % z(-1)
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initialconditions(2,3) = .4; % z(-2)
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initialconditions(3,4) = .5; % x(-1)
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initialconditions(2,4) = .6; % x(-2)
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initialconditions(1,4) = .7; % x(-3)
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initialconditions = ...
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dseries(initialconditions, dates('2000Q1'), {'foo', 'y','z', 'x'});
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set_dynare_seed('default');
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ts = simul_backward_model(initialconditions, 100);
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ex = load('example.mat');
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end;
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if max(max(abs(example.g1-g1)))>1e-8
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error('Jacobian matrices do not match!')
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end
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delete('example.mat')
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||||
delete('example.mat')
|
||||
if max(abs(ex.foo-ts.foo.data))>1e-12
|
||||
error('Simulations do not match!')
|
||||
end
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
// --+ options: stochastic,transform_unary_ops,json=compute +--
|
||||
|
||||
var foo x1 x2 x1bar x2bar;
|
||||
|
||||
varexo ex1 ex2 ex1bar ex2bar;
|
||||
|
||||
parameters a_x1_0 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
|
||||
beta
|
||||
alpha ;
|
||||
|
||||
a_x1_0 = -.9;
|
||||
a_x1_0_ = -.8;
|
||||
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 = 1/(1+.02);
|
||||
alpha = .5;
|
||||
|
||||
// Define a TREND_COMPONENT model from a subset of equations in the model block.
|
||||
trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
|
||||
|
||||
/* Define a VAR_EXPECTATION_MODEL
|
||||
** ------------------------------
|
||||
**
|
||||
** model_name: the name of the VAR_EXPECTATION_MODEL (mandatory).
|
||||
** auxiliary_model_name: the name of the VAR model used for the expectations (mandatory).
|
||||
** variable: the name of the variable to be forecasted (mandatory).
|
||||
** horizon: the horizon forecast (mandatory).
|
||||
** discount: the discount factor, which can be a value or a declared parameter (default is 1.0, no discounting).
|
||||
**
|
||||
**
|
||||
** The `horizon` parameter can be an integer in which case the (discounted) `horizon` step ahead forecast
|
||||
** is computed using the VAR model `var_model_name`. Alternatively, `horizon` can be a range. In this case
|
||||
** VAR_EXPECTATION_MODEL returns a discounted sum of expected values. If `horizon` is set equal to the range
|
||||
** 0:Inf, then VAR_EXPECTATION_MODEL computes:
|
||||
**
|
||||
** ∑ βʰ Eₜ[yₜ₊ₕ]
|
||||
**
|
||||
** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
|
||||
*/
|
||||
|
||||
var_expectation_model(model_name = varexp, expression = x1/100-alpha*x2, auxiliary_model_name = toto, horizon = 15:50, discount = beta) ;
|
||||
|
||||
|
||||
model;
|
||||
|
||||
[name='eq:x1', data_type='nonstationary']
|
||||
diff(x1) = a_x1_0*(x1(-1)-x1bar(-1))+a_x1_0_*(x2(-1)-x2bar(-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', data_type='nonstationary']
|
||||
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', data_type='nonstationary']
|
||||
x1bar = x1bar(-1) + ex1bar;
|
||||
|
||||
[name='eq:x2bar', data_type='nonstationary']
|
||||
x2bar = x2bar(-1) + ex2bar;
|
||||
|
||||
foo = .5*foo(-1) + var_expectation(varexp);
|
||||
end;
|
||||
|
||||
// Initialize the VAR expectation model, will build the companion matrix of the VAR.
|
||||
var_expectation.initialize('varexp')
|
||||
|
||||
// Update VAR_EXPECTATION reduced form parameters
|
||||
var_expectation.update('varexp');
|
||||
|
||||
weights = M_.params(M_.var_expectation.varexp.param_indices);
|
||||
|
||||
if ~all(weights(1:6)) || ~all(weights(9:10)) || weights(7) || weights(8) || weights(11) || weights(12)
|
||||
error('Wrong reduced form parameter for VAR_EXPECTATION_MODEL')
|
||||
end
|
||||
Loading…
Reference in New Issue