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Add linear combination support for growth neutrality in Iiterative OLS.

time-shift
DoraK 2019-10-07 16:45:24 +02:00 committed by Stéphane Adjemian (Charybdis)
parent 8f6647b557
commit 41c66583ac
9 changed files with 185 additions and 53 deletions
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34
matlab/+pac/+estimate/iterative_ols.m
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@ -417,25 +417,21 @@ function [PacExpectations, Model] = UpdatePacExpectationsData(dataPAC0, dataPAC1
end
% Add correction for growth neutrality if required.
correction = 0;
if isfield(Model.pac.(pacmodl), 'growth_type')
if numel(Model.pac.(pacmodl).growth_type) > 1 || ...
Model.pac.(pacmodl).growth_constant(1) ~= 1 || ...
Model.pac.(pacmodl).growth_param_id(1) ~= 0
error('Linear combinations in growth parameter are not yet supported')
end
switch Model.pac.(pacmodl).growth_type{1}
case 'parameter'
correction = Model.params(Model.pac.(pacmodl).growth_index(1))*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index);
case 'exogenous'
GrowthVariable = data{Model.exo_names{Model.pac.(pacmodl).growth_index(1)}};
GrowthVariable = GrowthVariable(range).data;
correction = GrowthVariable*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index);
case 'endogenous'
GrowthVariable = data{Model.endo_names{Model.pac.(pacmodl).growth_index(1)}}.lag(abs(Model.pac.(pacmodl).growth_lag(1)));
GrowthVariable = GrowthVariable(range).data;
correction = GrowthVariable*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index);
otherwise
error('Not yet implemented.')
if isfield(Model.pac.(pacmodl), 'growth_linear_comb')
for iter = 1:numel(Model.pac.(pacmodl).growth_linear_comb)
GrowthVariable = Model.pac.(pacmodl).growth_linear_comb(iter).constant;
if Model.pac.(pacmodl).growth_linear_comb(iter).param_id > 0
GrowthVariable = GrowthVariable*Model.params(Model.pac.(pacmodl).growth_linear_comb(iter).param_id);
end
if Model.pac.(pacmodl).growth_linear_comb(iter).exo_id > 0
GrowthVariable = GrowthVariable*data{Model.exo_names{Model.pac.(pacmodl).growth_linear_comb(iter).exo_id}}.lag(abs(Model.pac.(pacmodl).growth_linear_comb(iter).lag));
GrowthVariable = GrowthVariable(range).data;
elseif Model.pac.(pacmodl).growth_linear_comb(iter).endo_id > 0
GrowthVariable = GrowthVariable*data{Model.endo_names{Model.pac.(pacmodl).growth_linear_comb(iter).endo_id}}.lag(abs(Model.pac.(pacmodl).growth_linear_comb(iter).lag));
GrowthVariable = GrowthVariable(range).data;
end
correction = correction + GrowthVariable;
end
correction = correction*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index);
end
PacExpectations = PacExpectations+correction;

2
matlab/+pac/+update/parameters.m
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@ -122,7 +122,7 @@ for e=1:number_of_pac_eq
% Get the value of the discount factor.
beta = DynareModel.params(pacmodel.discount_index);
% Is growth argument passed to pac_expectation?
if isfield(pacmodel, 'growth_index')
if isfield(pacmodel, 'growth_str')
growth_flag = true;
else
growth_flag = false;

75
matlab/print_expectations.m
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@ -127,7 +127,7 @@ switch expectationmodelkind
fprintf(fid, '%s;\n\n', parameter_declaration);
if withcalibration
for i=1:length(expectationmodel.param_indices)
fprintf(fid, '%s = %s;\n', M_.param_names{expectationmodel.param_indices(i)}, num2str(M_.params(expectationmodel.param_indices(i)), 16));
fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.param_indices(i)}, M_.params(expectationmodel.param_indices(i)));
end
end
case 'pac'
@ -139,7 +139,7 @@ switch expectationmodelkind
fprintf(fid, '%s;\n\n', parameter_declaration);
if withcalibration
for i=1:length(expectationmodel.equations.(eqtag).h0_param_indices)
fprintf(fid, '%s = %s;\n', M_.param_names{expectationmodel.equations.(eqtag).h0_param_indices(i)}, num2str(M_.params(expectationmodel.equations.(eqtag).h0_param_indices(i)), 16));
fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.equations.(eqtag).h0_param_indices(i)}, M_.params(expectationmodel.equations.(eqtag).h0_param_indices(i)));
end
end
end
@ -151,7 +151,7 @@ switch expectationmodelkind
fprintf(fid, '%s;\n\n', parameter_declaration);
if withcalibration
for i=1:length(expectationmodel.equations.(eqtag).h1_param_indices)
fprintf(fid, '%s = %s;\n', M_.param_names{expectationmodel.equations.(eqtag).h1_param_indices(i)}, num2str(M_.params(expectationmodel.equations.(eqtag).h1_param_indices(i)), 16));
fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.equations.(eqtag).h1_param_indices(i)}, M_.params(expectationmodel.equations.(eqtag).h1_param_indices(i)));
end
end
end
@ -159,7 +159,7 @@ switch expectationmodelkind
fprintf(fid, '\n');
fprintf(fid, 'parameters %s;\n\n', M_.param_names{expectationmodel.growth_neutrality_param_index});
if withcalibration
fprintf(fid, '%s = %s;\n', M_.param_names{expectationmodel.growth_neutrality_param_index}, num2str(M_.params(expectationmodel.growth_neutrality_param_index), 16));
fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.growth_neutrality_param_index}, M_.params(expectationmodel.growth_neutrality_param_index));
end
growth_correction = true;
else
@ -307,42 +307,55 @@ for i=1:maxlag
end
if isequal(id, 1)
if isequal(expectationmodelkind, 'pac') && growth_correction
if numel(expectationmodel.growth_type) > 1 || ...
expectationmodel.growth_constant(1) ~= 1 || ...
expectationmodel.growth_param_id(1) ~= 0
error('Linear combinations in growth parameter are not yet supported')
end
pgrowth = M_.params(expectationmodel.growth_neutrality_param_index);
vgrowth = expectationmodel.growth_str;
switch expectationmodel.growth_type{1}
case 'parameter'
vgrowth = M_.params(strcmp(vgrowth, M_.param_names));
otherwise
vgrowth = regexprep(vgrowth, '\<(?!diff\>)\<(?!log\>)\<(?!\d\>)\w+', 'dbase.$0');
linearCombination = '';
for iter = 1:numel(expectationmodel.growth_linear_comb)
vgrowth='';
if expectationmodel.growth_linear_comb(iter).exo_id > 0
vgrowth = strcat('dbase.', M_.exo_names{expectationmodel.growth_linear_comb(iter).exo_id});
elseif expectationmodel.growth_linear_comb(iter).endo_id > 0
vgrowth = strcat('dbase.', M_.endo_names{expectationmodel.growth_linear_comb(iter).endo_id});
end
if expectationmodel.growth_linear_comb(iter).lag ~= 0
vgrowth = sprintf('%s(%d)', vgrowth, expectationmodel.growth_linear_comb(iter).lag);
end
if expectationmodel.growth_linear_comb(iter).param_id > 0
if ~isempty(vgrowth)
vgrowth = sprintf('%1.16f*%s',M_.params(expectationmodel.growth_linear_comb(iter).param_id), vgrowth);
else
vgrowth = num2str(M_.params(expectationmodel.growth_linear_comb(iter).param_id), '%1.16f');
end
end
if abs(expectationmodel.growth_linear_comb(iter).constant) ~= 1
if ~isempty(vgrowth)
vgrowth = sprintf('%1.16f*%s', expectationmodel.growth_linear_comb(iter).constant, vgrowth);
else
vgrowth = num2str(expectationmodel.growth_linear_comb(iter).constant, '%1.16f');
end
end
if iter > 1
if expectationmodel.growth_linear_comb(iter).constant > 0
linearCombination = sprintf('%s+%s', linearCombination, vgrowth);
else
linearCombination = sprintf('%s-%s', linearCombination, vgrowth);
end
else
linearCombination=vgrowth;
end
end
if parameter>=0
switch expectationmodel.growth_type{1}
case 'parameter'
expression = sprintf('%s*%s+%s*%s', num2str(pgrowth, '%1.16f'), num2str(vgrowth, '%1.16f'), num2str(parameter, '%1.16f'), variable);
otherwise
expression = sprintf('%s*%s+%s*%s', num2str(pgrowth, '%1.16f'), vgrowth, num2str(parameter, '%1.16f'), variable);
end
if parameter >= 0
expression = sprintf('%1.16f*(%s)+%1.16f*%s', pgrowth, linearCombination, parameter, variable);
else
switch expectationmodel.growth_type{1}
case 'parameter'
expression = sprintf('%s*%s-%s*%s', num2str(pgrowth, '%1.16f'), num2str(vgrowth, '%1.16f'), num2str(-parameter, '%1.16f'), variable);
otherwise
expression = sprintf('%s*%s-%s*%s', num2str(pgrowth, '%1.16f'), vgrowth, num2str(-parameter, '%1.16f'), variable);
end
expression = sprintf('%1.16f*(%s)-%1.16f*%s', pgrowth, linearCombination, -parameter, variable);
end
else
expression = sprintf('%s*%s', num2str(parameter, '%1.16f'), variable);
expression = sprintf('%1.16f*%s', parameter, variable);
end
else
if parameter>=0
expression = sprintf('%s + %s*%s', expression, num2str(parameter, '%1.16f'), variable);
expression = sprintf('%s+%1.16f*%s', expression, parameter, variable);
else
expression = sprintf('%s - %s*%s', expression, num2str(-parameter, '%1.16f'), variable);
expression = sprintf('%s-%1.16f*%s', expression, -parameter, variable);
end
end
end

6
matlab/write_expectations.m
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@ -143,7 +143,11 @@ for i=1:maxlag
end
if isfield(expectationmodel, 'growth_neutrality_param_index')
growthneutralitycorrection = sprintf('%s*%s', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
if numel(expectationmodel.growth_linear_comb) == 1
growthneutralitycorrection = sprintf('%s*%s', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
else
growthneutralitycorrection = sprintf('%s*(%s)', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
end
else
growthneutralitycorrection = '';
end

2
preprocessor

@ -1 +1 @@
Subproject commit d7e70a40630985a154f81631ef894cda7194db43
Subproject commit 979453815cf6dcfa9ec4f2f6e9eef48a4cfb3d58

1
tests/Makefile.am
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@ -439,6 +439,7 @@ ECB_MODFILES = \
pac/trend-component-19/example1.mod \
pac/trend-component-19/example2.mod \
pac/trend-component-19/example3.mod \
pac/trend-component-19-growth-lin-comb/example.mod \
pac/trend-component-1-mce/example_det.mod \
pac/trend-component-1-mce/example_sto.mod \
pac/trend-component-2-mce/example_det.mod \

1
tests/pac/run_all_tests.m
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@ -40,6 +40,7 @@ r = [r; run_this_test('trend-component-13b')];
r = [r; run_this_test('trend-component-14')];
r = [r; run_this_test('trend-component-15')];
r = [r; run_this_test('trend-component-16')];
r = [r; run_this_test('trend-component-19-growth-lin-comb')];
print_results(r);

8
tests/pac/trend-component-19-growth-lin-comb/clean Normal file
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@ -0,0 +1,8 @@
#!/bin/sh
rm -rf example
rm -rf +example
rm -f example.log
rm -f *.mat
rm -f *.m
rm -f *.dat

109
tests/pac/trend-component-19-growth-lin-comb/example.mod Normal file
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@ -0,0 +1,109 @@
// --+ options: json=compute, stochastic +--
var x1 x2 x1bar x2bar z y gg;
varexo ex1 ex2 ex1bar ex2bar ez ey g;
parameters
rho_1 rho_2
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 beta ;
rho_1 = .9;
rho_2 = -.2;
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;
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, growth=0.5*gg(-1)+beta+ex1, model_name=pacman);
model;
[name='eq:gg']
gg = g;
[name='eq:y']
y = rho_1*y(-1) + rho_2*y(-2) + ey;
[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) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
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 g = 0.1;
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');
// Set initial conditions to zero. Please use more sensible values if any...
initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
B = 1;
X = zeros(3,B);
set_dynare_seed('default');
options_.bnlms.set_dynare_seed_to_default = false;
for i=1:B
e_c_m = .5;
c_z_1 = .2;
c_z_2 = -.1;
// Simulate the model for 500 periods
TrueData = simul_backward_model(initialconditions, 300);
// Define a structure describing the parameters to be estimated (with initial conditions).
clear eparams
eparams.e_c_m = .5;
eparams.c_z_1 = .2;
eparams.c_z_2 =-.1;
// Define the dataset used for estimation
edata = TrueData;
edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
pac.print('pacman','zpac');
X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
end
mean(X, 2)