def test_denhaan_errors():
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = global_solve(model,
interp_type='smolyak',
smolyak_order=4,
verbose=False)
sigma = model.covariances
model.sigma = sigma
from dolo.algos.accuracy import denhaanerrors
denerr_1 = denhaanerrors(model, dr)
denerr_2 = denhaanerrors(model, dr_global)
print(denerr_1)
print(denerr_2)
print(denerr_2['max_errors'][0])
assert (max(denerr_2['max_errors']) < 10 - 7
) # errors with solyak colocations at order 4 are very small
def test_omega_errors():
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = global_solve(model,
smolyak_order=3,
verbose=False,
pert_order=1)
sigma = model.covariances
model.sigma = sigma
s_0 = dr.S_bar
from dolo.algos.accuracy import omega
res_1 = omega(model, dr, orders=[10, 10], time_discount=0.96)
res_2 = omega(model, dr_global)
print(res_1)
print(res_2)
def test_denhaan_errors():
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = global_solve(model, interp_type='smolyak', smolyak_order=4, verbose=False)
sigma = model.covariances
model.sigma = sigma
from dolo.algos.accuracy import denhaanerrors
denerr_1 = denhaanerrors(model, dr)
denerr_2 = denhaanerrors(model, dr_global)
print(denerr_1)
print(denerr_2)
print(denerr_2['max_errors'][0])
assert( max(denerr_2['max_errors']) < 10-7) # errors with solyak colocations at order 4 are very small
def test_omega_errors():
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = global_solve(model, smolyak_order=3, verbose=False, pert_order=1)
sigma = model.covariances
model.sigma = sigma
s_0 = dr.S_bar
from dolo.algos.accuracy import omega
res_1 = omega( model, dr, orders=[10,10], time_discount=0.96)
res_2 = omega( model, dr_global)
print(res_1)
print(res_2)
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
if __name__ == '__main__':
from dolo import *
model = yaml_import("/home/pablo/Documents/Research/Thesis/chapter_1/code/portfolios.yaml")
model.set_calibration(dumb=1)
from dolo.algos.perturbations import approximate_controls
dr = approximate_controls(model)
print(dr)
def time_iteration(model,
bounds=None,
verbose=False,
initial_dr=None,
pert_order=1,
with_complementarities=True,
interp_type='smolyak',
smolyak_order=3,
interp_orders=None,
maxit=500,
tol=1e-8,
integration='gauss-hermite',
integration_orders=None,
T=200,
n_s=3,
hook=None):
"""Finds a global solution for ``model`` using backward time-iteration.
Parameters:
-----------
model: NumericModel
"fg" or "fga" model to be solved
bounds: ndarray
boundaries for approximations. First row contains minimum values. Second row contains maximum values.
verbose: boolean
if True, display iterations
initial_dr: decision rule
initial guess for the decision rule
pert_order: {1}
if no initial guess is supplied, the perturbation solution at order ``pert_order`` is used as initial guess
with_complementarities: boolean (True)
if False, complementarity conditions are ignored
interp_type: {`smolyak`, `spline`}
type of interpolation to use for future controls
smolyak_orders: int
parameter ``l`` for Smolyak interpolation
interp_orders: 1d array-like
list of integers specifying the number of nods in each dimension if ``interp_type="spline" ``
Returns:
--------
decision rule object (SmolyakGrid or MultivariateSplines)
"""
def vprint(t):
if verbose:
print(t)
parms = model.calibration['parameters']
sigma = model.covariances
if initial_dr == None:
if pert_order == 1:
from dolo.algos.perturbations import approximate_controls
initial_dr = approximate_controls(model)
if pert_order > 1:
raise Exception("Perturbation order > 1 not supported (yet).")
if interp_type == 'perturbations':
return initial_dr
if bounds is not None:
pass
elif model.options and 'approximation_space' in model.options:
vprint('Using bounds specified by model')
approx = model.options['approximation_space']
a = approx['a']
b = approx['b']
bounds = numpy.row_stack([a, b])
bounds = numpy.array(bounds, dtype=float)
else:
vprint('Using asymptotic bounds given by first order solution.')
from dolo.numeric.timeseries import asymptotic_variance
# this will work only if initial_dr is a Taylor expansion
Q = asymptotic_variance(initial_dr.A.real,
initial_dr.B.real,
initial_dr.sigma,
T=T)
devs = numpy.sqrt(numpy.diag(Q))
bounds = numpy.row_stack([
initial_dr.S_bar - devs * n_s,
initial_dr.S_bar + devs * n_s,
])
if interp_orders == None:
interp_orders = [5] * bounds.shape[1]
if interp_type == 'smolyak':
from dolo.numeric.interpolation.smolyak import SmolyakGrid
dr = SmolyakGrid(bounds[0, :], bounds[1, :], smolyak_order)
elif interp_type == 'spline':
from dolo.numeric.interpolation.splines import MultivariateSplines
dr = MultivariateSplines(bounds[0, :], bounds[1, :], interp_orders)
elif interp_type == 'multilinear':
from dolo.numeric.interpolation.multilinear import MultilinearInterpolator
dr = MultilinearInterpolator(bounds[0, :], bounds[1, :], interp_orders)
if integration == 'optimal_quantization':
from dolo.numeric.discretization import quantization_nodes
# number of shocks
[epsilons, weights] = quantization_nodes(N_e, sigma)
elif integration == 'gauss-hermite':
from dolo.numeric.discretization import gauss_hermite_nodes
if not integration_orders:
integration_orders = [3] * sigma.shape[0]
[epsilons, weights] = gauss_hermite_nodes(integration_orders, sigma)
vprint('Starting time iteration')
# TODO: transpose
grid = dr.grid
xinit = initial_dr(grid)
xinit = xinit.real # just in case...
from dolo.algos.convert import get_fg_functions
f, g = get_fg_functions(model)
import time
fun = lambda x: step_residual(grid, x, dr, f, g, parms, epsilons, weights)
##
t1 = time.time()
err = 1
x0 = xinit
it = 0
verbit = True if verbose == 'full' else False
if with_complementarities:
lbfun = model.functions['arbitrage_lb']
ubfun = model.functions['arbitrage_ub']
lb = lbfun(grid, parms)
ub = ubfun(grid, parms)
else:
lb = None
ub = None
if verbose:
headline = '|{0:^4} | {1:10} | {2:8} | {3:8} | {4:3} |'.format(
'N', ' Error', 'Gain', 'Time', 'nit')
stars = '-' * len(headline)
print(stars)
print(headline)
print(stars)
err_0 = 1
while err > tol and it < maxit:
t_start = time.time()
it += 1
dr.set_values(x0)
from dolo.numeric.optimize.newton import serial_newton, SerialDifferentiableFunction
from dolo.numeric.optimize.ncpsolve import ncpsolve
sdfun = SerialDifferentiableFunction(fun)
if with_complementarities:
[x, nit] = ncpsolve(sdfun, lb, ub, x0, verbose=verbit)
else:
[x, nit] = serial_newton(sdfun, x0, verbose=verbit)
err = abs(x - x0).max()
err_SA = err / err_0
err_0 = err
t_finish = time.time()
elapsed = t_finish - t_start
if verbose:
print('|{0:4} | {1:10.3e} | {2:8.3f} | {3:8.3f} | {4:3} |'.format(
it, err, err_SA, elapsed, nit))
x0 = x0 + (x - x0)
if hook:
hook(dr, it, err)
if False in np.isfinite(x0):
print('iteration {} failed : non finite value')
return [x0, x]
if it == maxit:
import warnings
warnings.warn(UserWarning("Maximum number of iterations reached"))
t2 = time.time()
if verbose:
print(stars)
print('Elapsed: {} seconds.'.format(t2 - t1))
print(stars)
return dr
def time_iteration(model, bounds=None, verbose=False, initial_dr=None,
pert_order=1, with_complementarities=True,
interp_type='smolyak', smolyak_order=3, interp_orders=None,
maxit=500, tol=1e-8,
integration='gauss-hermite', integration_orders=None,
T=200, n_s=3, hook=None):
"""Finds a global solution for ``model`` using backward time-iteration.
Parameters:
-----------
model: NumericModel
"fg" or "fga" model to be solved
bounds: ndarray
boundaries for approximations. First row contains minimum values. Second row contains maximum values.
verbose: boolean
if True, display iterations
initial_dr: decision rule
initial guess for the decision rule
pert_order: {1}
if no initial guess is supplied, the perturbation solution at order ``pert_order`` is used as initial guess
with_complementarities: boolean (True)
if False, complementarity conditions are ignored
interp_type: {`smolyak`, `spline`}
type of interpolation to use for future controls
smolyak_orders: int
parameter ``l`` for Smolyak interpolation
interp_orders: 1d array-like
list of integers specifying the number of nods in each dimension if ``interp_type="spline" ``
Returns:
--------
decision rule object (SmolyakGrid or MultivariateSplines)
"""
def vprint(t):
if verbose:
print(t)
parms = model.calibration['parameters']
sigma = model.covariances
if initial_dr == None:
if pert_order==1:
from dolo.algos.perturbations import approximate_controls
initial_dr = approximate_controls(model)
if pert_order>1:
raise Exception("Perturbation order > 1 not supported (yet).")
if interp_type == 'perturbations':
return initial_dr
if bounds is not None:
pass
elif model.options and 'approximation_space' in model.options:
vprint('Using bounds specified by model')
approx = model.options['approximation_space']
a = approx['a']
b = approx['b']
bounds = numpy.row_stack([a, b])
bounds = numpy.array(bounds, dtype=float)
else:
vprint('Using asymptotic bounds given by first order solution.')
from dolo.numeric.timeseries import asymptotic_variance
# this will work only if initial_dr is a Taylor expansion
Q = asymptotic_variance(initial_dr.A.real, initial_dr.B.real, initial_dr.sigma, T=T)
devs = numpy.sqrt(numpy.diag(Q))
bounds = numpy.row_stack([
initial_dr.S_bar - devs * n_s,
initial_dr.S_bar + devs * n_s,
])
if interp_orders == None:
interp_orders = [5] * bounds.shape[1]
if interp_type == 'smolyak':
from dolo.numeric.interpolation.smolyak import SmolyakGrid
dr = SmolyakGrid(bounds[0, :], bounds[1, :], smolyak_order)
elif interp_type == 'spline':
from dolo.numeric.interpolation.splines import MultivariateSplines
dr = MultivariateSplines(bounds[0, :], bounds[1, :], interp_orders)
elif interp_type == 'multilinear':
from dolo.numeric.interpolation.multilinear import MultilinearInterpolator
dr = MultilinearInterpolator(bounds[0, :], bounds[1, :], interp_orders)
if integration == 'optimal_quantization':
from dolo.numeric.discretization import quantization_nodes
# number of shocks
[epsilons, weights] = quantization_nodes(N_e, sigma)
elif integration == 'gauss-hermite':
from dolo.numeric.discretization import gauss_hermite_nodes
if not integration_orders:
integration_orders = [3] * sigma.shape[0]
[epsilons, weights] = gauss_hermite_nodes(integration_orders, sigma)
vprint('Starting time iteration')
# TODO: transpose
grid = dr.grid
xinit = initial_dr(grid)
xinit = xinit.real # just in case...
from dolo.algos.convert import get_fg_functions
f,g = get_fg_functions(model)
import time
fun = lambda x: step_residual(grid, x, dr, f, g, parms, epsilons, weights)
##
t1 = time.time()
err = 1
x0 = xinit
it = 0
verbit = True if verbose=='full' else False
if with_complementarities:
lbfun = model.functions['arbitrage_lb']
ubfun = model.functions['arbitrage_ub']
lb = lbfun(grid, parms)
ub = ubfun(grid, parms)
else:
lb = None
ub = None
if verbose:
headline = '|{0:^4} | {1:10} | {2:8} | {3:8} | {4:3} |'.format( 'N',' Error', 'Gain','Time', 'nit' )
stars = '-'*len(headline)
print(stars)
print(headline)
print(stars)
err_0 = 1
while err > tol and it < maxit:
t_start = time.time()
it +=1
dr.set_values(x0)
from dolo.numeric.optimize.newton import serial_newton, SerialDifferentiableFunction
from dolo.numeric.optimize.ncpsolve import ncpsolve
sdfun = SerialDifferentiableFunction(fun)
if with_complementarities:
[x,nit] = ncpsolve(sdfun, lb, ub, x0, verbose=verbit)
else:
[x,nit] = serial_newton(sdfun, x0, verbose=verbit)
err = abs(x-x0).max()
err_SA = err/err_0
err_0 = err
t_finish = time.time()
elapsed = t_finish - t_start
if verbose:
print('|{0:4} | {1:10.3e} | {2:8.3f} | {3:8.3f} | {4:3} |'.format( it, err, err_SA, elapsed, nit ))
x0 = x0 + (x-x0)
if hook:
hook(dr,it,err)
if False in np.isfinite(x0):
print('iteration {} failed : non finite value')
return [x0, x]
if it == maxit:
import warnings
warnings.warn(UserWarning("Maximum number of iterations reached"))
t2 = time.time()
if verbose:
print(stars)
print('Elapsed: {} seconds.'.format(t2 - t1))
print(stars)
return dr
