Added integration tests (Iterative OLS and NLS for PAC equations).
parent
cf35496c06
commit
6b113273d3
|
@ -405,9 +405,11 @@ MODFILES = \
|
|||
pac/trend-component-12/example.mod \
|
||||
pac/trend-component-13a/example.mod \
|
||||
pac/trend-component-13b/example.mod \
|
||||
estimation/univariate/bayesian.mod \
|
||||
pac/trend-component-14/example.mod \
|
||||
pac/trend-component-14/substitution.mod \
|
||||
pac/trend-component-15/example.mod \
|
||||
pac/trend-component-16/example.mod \
|
||||
estimation/univariate/bayesian.mod \
|
||||
dynare-command-options/ramst.mod
|
||||
|
||||
PARTICLEFILES = \
|
||||
|
|
|
@ -37,6 +37,9 @@ r = [r; run_this_test('trend-component-11')];
|
|||
r = [r; run_this_test('trend-component-12')];
|
||||
r = [r; run_this_test('trend-component-13a')];
|
||||
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')];
|
||||
|
||||
print_results(r);
|
||||
|
||||
|
|
|
@ -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
|
|
@ -0,0 +1,138 @@
|
|||
// --+ options: json=compute, stochastic +--
|
||||
|
||||
var x1 x2 x1bar x2bar z y x;
|
||||
|
||||
varexo ex1 ex2 ex1bar ex2bar ez ey ex;
|
||||
|
||||
parameters
|
||||
rho_1 rho_2 rho_3 rho_4
|
||||
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
|
||||
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;
|
||||
|
||||
lambda = .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);
|
||||
|
||||
model;
|
||||
|
||||
[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: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)) + (1-lambda)*( y + x) + 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 ex = 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));
|
||||
|
||||
// Simulate the model for 500 periods
|
||||
TrueData = simul_backward_model(initialconditions, 5000);
|
||||
|
||||
// Define a structure describing the parameters to be estimated (with initial conditions).
|
||||
clear eparams
|
||||
eparams.e_c_m = .9;
|
||||
eparams.c_z_1 = .5;
|
||||
eparams.c_z_2 = .2;
|
||||
|
||||
// Define the dataset used for estimation
|
||||
edata = TrueData;
|
||||
edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
|
||||
pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2005Q1+4000);
|
||||
|
||||
e_c_m_iterative_ols = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
|
||||
c_z_1_iterative_ols = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
|
||||
c_z_2_iterative_ols = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
|
||||
|
||||
disp(sprintf('Estimate of e_c_m: %f', e_c_m_iterative_ols))
|
||||
disp(sprintf('Estimate of c_z_1: %f', c_z_1_iterative_ols))
|
||||
disp(sprintf('Estimate of c_z_2: %f', c_z_2_iterative_ols))
|
||||
|
||||
skipline(2)
|
||||
|
||||
clear eparams
|
||||
eparams.e_c_m = .9;
|
||||
eparams.c_z_1 = .5;
|
||||
eparams.c_z_2 = .2;
|
||||
|
||||
edata = TrueData;
|
||||
edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
|
||||
pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+4000, 'fminunc');
|
||||
|
||||
e_c_m_nls = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
|
||||
c_z_1_nls = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
|
||||
c_z_2_nls = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
|
||||
|
||||
disp(sprintf('Estimate of e_c_m: %f', e_c_m_nls))
|
||||
disp(sprintf('Estimate of c_z_1: %f', c_z_1_nls))
|
||||
disp(sprintf('Estimate of c_z_2: %f', c_z_2_nls))
|
||||
|
||||
if abs(e_c_m_nls-e_c_m_iterative_ols)>.01
|
||||
error('Iterative OLS and NLS do not provide consistent estimates (e_c_m)')
|
||||
end
|
||||
|
||||
if abs(c_z_1_nls-c_z_1_iterative_ols)>.01
|
||||
error('Iterative OLS and NLS do not provide consistent estimates (c_z_1)')
|
||||
end
|
||||
|
||||
if abs(c_z_2_nls-c_z_2_iterative_ols)>.01
|
||||
error('Iterative OLS and NLS do not provide consistent estimates (c_z_2)')
|
||||
end
|
|
@ -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
|
|
@ -0,0 +1,151 @@
|
|||
// --+ options: json=compute, stochastic +--
|
||||
|
||||
var x1 x2 x1bar x2bar z y x;
|
||||
|
||||
varexo ex1 ex2 ex1bar ex2bar ez ey ex;
|
||||
|
||||
parameters
|
||||
rho_1 rho_2 rho_3 rho_4
|
||||
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
|
||||
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;
|
||||
|
||||
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);
|
||||
|
||||
model;
|
||||
|
||||
[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: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)) + (1-lambda)*( y + x) + 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 ex = 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));
|
||||
|
||||
// Simulate the model for 500 periods
|
||||
TrueData = simul_backward_model(initialconditions, 5000);
|
||||
|
||||
// Define a structure describing the parameters to be estimated (with initial conditions).
|
||||
clear eparams
|
||||
eparams.e_c_m = .9;
|
||||
eparams.c_z_1 = .5;
|
||||
eparams.c_z_2 = .2;
|
||||
eparams.lambda = .7;
|
||||
|
||||
// Define the dataset used for estimation
|
||||
edata = TrueData;
|
||||
edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
|
||||
pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2005Q1+4000);
|
||||
|
||||
e_c_m_iterative_ols = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
|
||||
c_z_1_iterative_ols = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
|
||||
c_z_2_iterative_ols = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
|
||||
lambda_iterative_ols = M_.params(strmatch('lambda', M_.param_names, 'exact'));
|
||||
|
||||
disp(sprintf('Estimate of e_c_m: %f', e_c_m_iterative_ols))
|
||||
disp(sprintf('Estimate of c_z_1: %f', c_z_1_iterative_ols))
|
||||
disp(sprintf('Estimate of c_z_2: %f', c_z_2_iterative_ols))
|
||||
disp(sprintf('Estimate of lambda: %f', lambda_iterative_ols))
|
||||
|
||||
skipline(2)
|
||||
|
||||
// Define a structure describing the parameters to be estimated (with initial conditions).
|
||||
clear eparams
|
||||
eparams.e_c_m = .9;
|
||||
eparams.c_z_1 = .5;
|
||||
eparams.c_z_2 = .2;
|
||||
eparams.lambda = .7;
|
||||
|
||||
|
||||
edata = TrueData;
|
||||
edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
|
||||
pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+4000, 'csminwel');
|
||||
|
||||
|
||||
e_c_m_nls = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
|
||||
c_z_1_nls = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
|
||||
c_z_2_nls = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
|
||||
lambda_nls = M_.params(strmatch('lambda', M_.param_names, 'exact'));
|
||||
|
||||
disp(sprintf('Estimate of e_c_m: %f', e_c_m_nls))
|
||||
disp(sprintf('Estimate of c_z_1: %f', c_z_1_nls))
|
||||
disp(sprintf('Estimate of c_z_2: %f', c_z_2_nls))
|
||||
disp(sprintf('Estimate of lambda: %f', lambda_nls))
|
||||
|
||||
if abs(e_c_m_nls-e_c_m_iterative_ols)>.01
|
||||
error('Iterative OLS and NLS do not provide consistent estimates (e_c_m)')
|
||||
end
|
||||
|
||||
if abs(c_z_1_nls-c_z_1_iterative_ols)>.01
|
||||
error('Iterative OLS and NLS do not provide consistent estimates (c_z_1)')
|
||||
end
|
||||
|
||||
if abs(c_z_2_nls-c_z_2_iterative_ols)>.01
|
||||
error('Iterative OLS and NLS do not provide consistent estimates (c_z_2)')
|
||||
end
|
||||
|
||||
if abs(lambda_nls-lambda_iterative_ols)>.01
|
||||
error('Iterative OLS and NLS do not provide consistent estimates (lambda)')
|
||||
end
|
Loading…
Reference in New Issue