def solve_model_around_risky_ss(model,
verbose=False,
return_dr=True,
initial_sol=None):
#model = yaml_import(filename)
if initial_sol == None:
if 'model_type' in model and model['model_type'] == 'portfolios':
print(
'This is a portfolio model ! Converting to deterministic one.')
from portfolio_perturbation import portfolios_to_deterministic
model = portfolios_to_deterministic(model, ['x_1', 'x_2'])
model.check()
from dolo.numeric.perturbations_to_states import approximate_controls
perturb_sol = approximate_controls(model,
order=1,
return_dr=False,
substitute_auxiliary=True)
[X_bar, X_s] = perturb_sol
else:
perturb_sol = initial_sol
[X_bar, X_s] = perturb_sol
# reduce X_s to the real controls (remove auxiliary variables
X_s = X_s[:len(model['variables_groups']['controls']), :]
X_bar = X_bar[:len(model['variables_groups']['controls'])]
X_bar = numpy.array(X_bar)
if abs(X_s.imag).max() < 1e-10:
X_s = X_s.real
else:
raise (Exception('Complex decision rule'))
print('Perturbation solution found')
#print model.parameters
#exit()
X_bar_1 = X_bar
X_s_1 = X_s
#model = yaml_import(filename)
#from dolo.symbolic.symbolic import Parameter
[S_bar, X_bar, X_s, P] = solve_risky_ss(model, X_bar, X_s, verbose=verbose)
if return_dr:
cdr = CDR([S_bar, X_bar, X_s])
# cdr.P = P
return cdr
return [S_bar, X_bar, X_s, P]
def approximate_controls(model,
order=1,
lambda_name=None,
return_dr=True,
steady_state=None,
verbose=True):
assert (model.model_type == 'dtcscc')
import numpy
[f_fun, g_fun] = model_to_fg(model, order=order)
parms = model.calibration['parameters']
distrib = model.get_distribution()
sigma = distrib.sigma
if steady_state is None:
calibration = model.calibration
else:
calibration = steady_state
states_ss = calibration['states']
controls_ss = calibration['controls']
shocks_ss = calibration['shocks']
f_args_ss = numpy.concatenate(
[states_ss, controls_ss, states_ss, controls_ss])
g_args_ss = numpy.concatenate([states_ss, controls_ss, shocks_ss])
f = f_fun(f_args_ss, parms, order=order)
g = g_fun(g_args_ss, parms, order=order)
if lambda_name:
epsilon = 0.001
sigma_index = [p.name for p in model.parameters].index(lambda_name)
pert_parms = parms.copy()
pert_parms[sigma_index] += epsilon
g_pert = g_fun(g_args_ss, pert_parms)
sig2 = (g_pert[0] - g[0]) / epsilon * 2
sig2_s = (g_pert[1] - g[1]) / epsilon * 2
pert_sol = state_perturb(f,
g,
sigma,
sigma2_correction=[sig2, sig2_s],
verbose=verbose)
else:
g = g_fun(g_args_ss, parms, order=order)
pert_sol = state_perturb(f, g, sigma, verbose=verbose)
n_s = len(states_ss)
n_c = len(controls_ss)
if order == 1:
if return_dr:
S_bar = numpy.array(states_ss)
X_bar = numpy.array(controls_ss)
# add transitions of states to the d.r.
X_s = pert_sol[0]
A = g[1][:, :n_s] + numpy.dot(g[1][:, n_s:n_s + n_c], X_s)
B = g[1][:, n_s + n_c:]
dr = CDR([S_bar, X_bar, X_s])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
return [controls_ss] + pert_sol
if order == 2:
[[X_s, X_ss], [X_tt]] = pert_sol
X_bar = controls_ss + X_tt / 2
if return_dr:
S_bar = states_ss
S_bar = numpy.array(S_bar)
X_bar = numpy.array(X_bar)
dr = CDR([S_bar, X_bar, X_s, X_ss])
A = g[1][:, :n_s] + numpy.dot(g[1][:, n_s:n_s + n_c], X_s)
B = g[1][:, n_s + n_c:]
dr.sigma = sigma
dr.A = A
dr.B = B
return dr
return [X_bar, X_s, X_ss]
if order == 3:
[[X_s, X_ss, X_sss], [X_tt, X_stt]] = pert_sol
X_bar = controls_ss + X_tt / 2
X_s = X_s + X_stt / 2
if return_dr:
S_bar = states_ss
dr = CDR([S_bar, X_bar, X_s, X_ss, X_sss])
dr.sigma = sigma
return dr
return [X_bar, X_s, X_ss, X_sss]
def approximate_controls(model, order=1, lambda_name=None, return_dr=True, steady_state=None, verbose=True, eigmax=1.0):
assert(model.model_type=='dtcscc')
[f_fun, g_fun] = model_to_fg(model, order=order)
import numpy
states = model.symbols['states']
controls = model.symbols['controls']
parms = model.calibration['parameters']
sigma = model.covariances
if steady_state is None:
calibration = model.calibration
else:
calibration = steady_state
states_ss = calibration['states']
controls_ss = calibration['controls']
shocks_ss = calibration['shocks']
f_args_ss = numpy.concatenate( [states_ss, controls_ss, states_ss, controls_ss] )
g_args_ss = numpy.concatenate( [states_ss, controls_ss, shocks_ss] )
f = f_fun( f_args_ss, parms, order=order)
g = g_fun( g_args_ss, parms, order=order)
if lambda_name:
epsilon = 0.001
sigma_index = [p.name for p in model.parameters].index(lambda_name)
pert_parms = parms.copy()
pert_parms[sigma_index] += epsilon
g_pert = g_fun(g_args_ss, pert_parms)
sig2 = (g_pert[0] - g[0])/epsilon*2
sig2_s = (g_pert[1] - g[1])/epsilon*2
pert_sol = state_perturb(f, g, sigma, sigma2_correction = [sig2, sig2_s], eigmax=eigmax, verbose=verbose)
else:
g = g_fun( g_args_ss, parms, order=order)
pert_sol = state_perturb(f, g, sigma, eigmax=eigmax, verbose=verbose )
n_s = len(states_ss)
n_c = len(controls_ss)
if order == 1:
if return_dr:
S_bar = numpy.array( states_ss )
X_bar = numpy.array( controls_ss )
# add transitions of states to the d.r.
X_s = pert_sol[0]
A = g[1][:,:n_s] + numpy.dot( g[1][:,n_s:n_s+n_c], X_s )
B = g[1][:,n_s+n_c:]
dr = CDR([S_bar, X_bar, X_s])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
return [controls_ss] + pert_sol
if order == 2:
[[X_s,X_ss],[X_tt]] = pert_sol
X_bar = controls_ss + X_tt/2
if return_dr:
S_bar = states_ss
S_bar = numpy.array(S_bar)
X_bar = numpy.array(X_bar)
dr = CDR([S_bar, X_bar, X_s, X_ss])
A = g[1][:,:n_s] + numpy.dot( g[1][:,n_s:n_s+n_c], X_s )
B = g[1][:,n_s+n_c:]
dr.sigma = sigma
dr.A = A
dr.B = B
return dr
return [X_bar, X_s, X_ss]
if order == 3:
[[X_s,X_ss,X_sss],[X_tt, X_stt]] = pert_sol
X_bar = controls_ss + X_tt/2
X_s = X_s + X_stt/2
if return_dr:
S_bar = states_ss
dr = CDR([S_bar, X_bar, X_s, X_ss, X_sss])
dr.sigma = sigma
return dr
return [X_bar, X_s, X_ss, X_sss]
def approximate_controls(model, order=1, lambda_name=None, substitute_auxiliary=False, return_dr=True, solve_systems=False):
[gm, g_fun, f_fun] = interim_gm(model, substitute_auxiliary, solve_systems, order)
# get steady_state
import numpy
[y0,x,parms] = model.read_calibration()
parms = numpy.array(parms)
y = y0
#y = model.solve_for_steady_state(y0)
sigma = numpy.array( model.read_covariances() ).astype(float)
states_ss = numpy.array([y[model.variables.index(v)] for v in gm['states']]).astype(float)
controls_ss = numpy.array([y[model.variables.index(v)] for v in gm['controls']]).astype(float)
shocks_ss = x
f_args_ss = numpy.concatenate( [states_ss, controls_ss, states_ss, controls_ss] )
g_args_ss = numpy.concatenate( [states_ss, controls_ss, shocks_ss] )
f = f_fun( f_args_ss, parms)
g = g_fun( g_args_ss, parms)
if lambda_name:
epsilon = 0.001
sigma_index = [p.name for p in model.parameters].index(lambda_name)
pert_parms = parms.copy()
pert_parms[sigma_index] += epsilon
g_pert = g_fun( g_args_ss, pert_parms)
sig2 = (g_pert[0] - g[0])/epsilon*2
sig2_s = (g_pert[1] - g[1])/epsilon*2
pert_sol = state_perturb(f, g, sigma, sigma2_correction = [sig2, sig2_s] )
else:
g = g_fun( g_args_ss, parms)
pert_sol = state_perturb(f, g, sigma )
n_s = len(states_ss)
n_c = len(controls_ss)
if order == 1:
if return_dr:
S_bar = numpy.array( states_ss )
X_bar = numpy.array( controls_ss )
# add transitions of states to the d.r.
X_s = pert_sol[0]
A = g[1][:,:n_s] + numpy.dot( g[1][:,n_s:n_s+n_c], X_s )
B = g[1][:,n_s+n_c:]
dr = CDR([S_bar, X_bar, X_s])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
return [controls_ss] + pert_sol
if order == 2:
[[X_s,X_ss],[X_tt]] = pert_sol
X_bar = controls_ss + X_tt/2
if return_dr:
S_bar = states_ss
S_bar = numpy.array(S_bar)
X_bar = numpy.array(X_bar)
dr = CDR([S_bar, X_bar, X_s, X_ss])
A = g[1][:,:n_s] + numpy.dot( g[1][:,n_s:n_s+n_c], X_s )
B = g[1][:,n_s+n_c:]
dr.sigma = sigma
dr.A = A
dr.B = B
return dr
return [X_bar, X_s, X_ss]
if order == 3:
[[X_s,X_ss,X_sss],[X_tt, X_stt]] = pert_sol
X_bar = controls_ss + X_tt/2
X_s = X_s + X_stt/2
if return_dr:
S_bar = states_ss
dr = CDR([S_bar, X_bar, X_s, X_ss, X_sss])
dr.sigma = sigma
return dr
return [X_bar, X_s, X_ss, X_sss]
def approximate_controls(model,
order=1,
lambda_name=None,
return_dr=True,
verbose=True):
from dolo.compiler.converter import model_to_fg
[g_fun, f_fun] = model_to_fg(model, order=order)
# get steady_state
import numpy
states = model.symbols_s['states']
controls = model.symbols_s['controls']
parms = model.calibration['parameters']
sigma = model.calibration['covariances']
states_ss = model.calibration['states']
controls_ss = model.calibration['controls']
shocks_ss = model.calibration['shocks']
f_args_ss = numpy.concatenate(
[states_ss, controls_ss, states_ss, controls_ss])
g_args_ss = numpy.concatenate([states_ss, controls_ss, shocks_ss])
f = f_fun(f_args_ss, parms)
g = g_fun(g_args_ss, parms)
if lambda_name:
epsilon = 0.001
sigma_index = [p.name for p in model.parameters].index(lambda_name)
pert_parms = parms.copy()
pert_parms[sigma_index] += epsilon
g_pert = g_fun(g_args_ss, pert_parms)
sig2 = (g_pert[0] - g[0]) / epsilon * 2
sig2_s = (g_pert[1] - g[1]) / epsilon * 2
pert_sol = state_perturb(f, g, sigma, sigma2_correction=[sig2, sig2_s])
else:
g = g_fun(g_args_ss, parms)
pert_sol = state_perturb(f, g, sigma)
n_s = len(states_ss)
n_c = len(controls_ss)
if order == 1:
if return_dr:
S_bar = numpy.array(states_ss)
X_bar = numpy.array(controls_ss)
# add transitions of states to the d.r.
X_s = pert_sol[0]
A = g[1][:, :n_s] + numpy.dot(g[1][:, n_s:n_s + n_c], X_s)
B = g[1][:, n_s + n_c:]
dr = CDR([S_bar, X_bar, X_s])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
return [controls_ss] + pert_sol
if order == 2:
[[X_s, X_ss], [X_tt]] = pert_sol
X_bar = controls_ss + X_tt / 2
if return_dr:
S_bar = states_ss
S_bar = numpy.array(S_bar)
X_bar = numpy.array(X_bar)
dr = CDR([S_bar, X_bar, X_s, X_ss])
A = g[1][:, :n_s] + numpy.dot(g[1][:, n_s:n_s + n_c], X_s)
B = g[1][:, n_s + n_c:]
dr.sigma = sigma
dr.A = A
dr.B = B
return dr
return [X_bar, X_s, X_ss]
if order == 3:
[[X_s, X_ss, X_sss], [X_tt, X_stt]] = pert_sol
X_bar = controls_ss + X_tt / 2
X_s = X_s + X_stt / 2
if return_dr:
S_bar = states_ss
dr = CDR([S_bar, X_bar, X_s, X_ss, X_sss])
dr.sigma = sigma
return dr
return [X_bar, X_s, X_ss, X_sss]
def approximate_controls(cmodel, order=1, lambda_name=None, return_dr=True):
from dolo.symbolic.model import SModel
if not isinstance(cmodel, SModel):
model = cmodel.model
else:
model = cmodel
from dolo.compiler.compiler_functions import model_to_fg
[g_fun, f_fun] = model_to_fg(model, order=order)
# get steady_state
import numpy
states = model.symbols_s['states']
controls = model.symbols_s['controls']
parms = model.calibration['parameters']
sigma = model.calibration['covariances']
states_ss = model.calibration['states']
controls_ss = model.calibration['controls']
shocks_ss = model.calibration['shocks']
f_args_ss = numpy.concatenate( [states_ss, controls_ss, states_ss, controls_ss] )
g_args_ss = numpy.concatenate( [states_ss, controls_ss, shocks_ss] )
f = f_fun( f_args_ss, parms)
g = g_fun( g_args_ss, parms)
if lambda_name:
epsilon = 0.001
sigma_index = [p.name for p in model.parameters].index(lambda_name)
pert_parms = parms.copy()
pert_parms[sigma_index] += epsilon
g_pert = g_fun(g_args_ss, pert_parms)
sig2 = (g_pert[0] - g[0])/epsilon*2
sig2_s = (g_pert[1] - g[1])/epsilon*2
pert_sol = state_perturb(f, g, sigma, sigma2_correction = [sig2, sig2_s] )
else:
g = g_fun( g_args_ss, parms)
pert_sol = state_perturb(f, g, sigma )
n_s = len(states_ss)
n_c = len(controls_ss)
if order == 1:
if return_dr:
S_bar = numpy.array( states_ss )
X_bar = numpy.array( controls_ss )
# add transitions of states to the d.r.
X_s = pert_sol[0]
A = g[1][:,:n_s] + numpy.dot( g[1][:,n_s:n_s+n_c], X_s )
B = g[1][:,n_s+n_c:]
dr = CDR([S_bar, X_bar, X_s])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
return [controls_ss] + pert_sol
if order == 2:
[[X_s,X_ss],[X_tt]] = pert_sol
X_bar = controls_ss + X_tt/2
if return_dr:
S_bar = states_ss
S_bar = numpy.array(S_bar)
X_bar = numpy.array(X_bar)
dr = CDR([S_bar, X_bar, X_s, X_ss])
A = g[1][:,:n_s] + numpy.dot( g[1][:,n_s:n_s+n_c], X_s )
B = g[1][:,n_s+n_c:]
dr.sigma = sigma
dr.A = A
dr.B = B
return dr
return [X_bar, X_s, X_ss]
if order == 3:
[[X_s,X_ss,X_sss],[X_tt, X_stt]] = pert_sol
X_bar = controls_ss + X_tt/2
X_s = X_s + X_stt/2
if return_dr:
S_bar = states_ss
dr = CDR([S_bar, X_bar, X_s, X_ss, X_sss])
dr.sigma = sigma
return dr
return [X_bar, X_s, X_ss, X_sss]
def approximate_controls(model, return_dr=True):
# get steady_state
import numpy
p = model.calibration['parameters']
sigma = model.calibration['covariances']
s = model.calibration['states'][:, None]
x = model.calibration['controls'][:, None]
e = model.calibration['shocks'][:, None]
from numpy.linalg import solve
g = model.functions['transition']
f = model.functions['arbitrage']
l = g(s, x, e, p, derivs=True)
[junk, g_s, g_x, g_e] = [el[..., 0] for el in l]
if model.model_type == "fg2":
l = f(s, x, e, s, x, p, derivs=True)
[res, f_s, f_x, f_e, f_S, f_X] = [el[..., 0] for el in l]
else:
l = f(s, x, s, x, p, derivs=True)
[res, f_s, f_x, f_S, f_X] = [el[..., 0] for el in l]
n_s = g_s.shape[0] # number of controls
n_x = g_x.shape[1] # number of states
n_e = g_e.shape[1]
n_v = n_s + n_x
A = row_stack([
column_stack([eye(n_s), zeros((n_s, n_x))]),
column_stack([-f_S, -f_X])
])
B = row_stack([column_stack([g_s, g_x]), column_stack([f_s, f_x])])
from dolo.numeric.extern.qz import qzordered
[S, T, Q, Z, eigval] = qzordered(A, B, n_s)
Q = Q.real # is it really necessary ?
Z = Z.real
Z11 = Z[:n_s, :n_s]
Z12 = Z[:n_s, n_s:]
Z21 = Z[n_s:, :n_s]
Z22 = Z[n_s:, n_s:]
S11 = S[:n_s, :n_s]
T11 = T[:n_s, :n_s]
# first order solution
C = solve(Z11.T, Z21.T).T
P = np.dot(solve(S11.T, Z11.T).T, solve(Z11.T, T11.T).T)
Q = g_e
s = s.ravel()
x = x.ravel()
A = g_s + dot(g_x, C)
B = g_e
dr = CDR([s, x, C])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
