def deterministic_solve(model, shocks=None, start_states=None, T=100, ignore_constraints=False, maxit=100, initial_guess=None, verbose=False, tol=1e-6):
'''Computes a perfect foresight simulation using a stacked-time algorithm.
The initial state is specified either by providing a series of exogenous shocks and assuming the model is initially
in equilibrium with the first value of the shock, or by specifying an initial value for the states.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
.. versionadded:: 1.6.0
Overrides the data type of the result.
order : {'C', 'F', 'A', or 'K'}, optional
.. versionadded:: 1.6.0
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of 'a', otherwise it will be a base-class array. Defaults
to True.
Parameters
----------
model : NumericModel
"fg" or "fga" model to be solved
shocks : ndarray
:math:`n_e\\times N` matrix containing :math:`N` realizations of the shocks. :math:`N` must be smaller than :math:`T`. The exogenous process is assumed to remain constant and equal to its last value after `N` periods.
start_states : ndarray or dict
a vector with the value of initial states, or a calibration dictionary with the initial values of states and controls
T : int
horizon for the perfect foresight simulation
maxit : int
maximum number of iteration for the nonlinear solver
verbose : boolean
if True, the solver displays iterations
tol : float
stopping criterium for the nonlinear solver
ignore_constraints : bool
if True, complementarity constraints are ignored.
Returns
-------
pandas dataframe
a dataframe with T+1 observations of the model variables along the simulation (states, controls, auxiliaries). The first observation is the steady-state corresponding to the first value of the shocks. The simulation should return
to a steady-state corresponding to the last value of the exogenous shocks.
'''
# TODO:
# if model.model_spec == 'fga':
# from dolo.compiler.converter import GModel_fg_from_fga
# model = GModel_fg_from_fga(model)
# definitions
n_s = len(model.calibration['states'])
n_x = len(model.calibration['controls'])
if shocks is None:
shocks = numpy.zeros( (len(model.calibration['shocks']),1))
# until last period, exogenous shock takes its last value
epsilons = numpy.zeros( (T+1, shocks.shape[1]))
epsilons[:(shocks.shape[0]-1),:] = shocks[1:,:]
epsilons[(shocks.shape[0]-1):,:] = shocks[-1:,:]
# final initial and final steady-states consistent with exogenous shocks
if isinstance(start_states,dict):
# at least that part is clear
start_equilibrium = start_states
start_s = start_equilibrium['states']
start_x = start_equilibrium['controls']
final_s = start_equilibrium['states']
final_x = start_equilibrium['controls']
elif isinstance(start_states, numpy.ndarray):
start_s = start_states
start_x = model.calibration['controls']
final_s = model.calibration['states']
final_x = model.calibration['controls']
else:
# raise Exception("You must compute initial calibration yourself")
final_dict = {model.symbols['shocks'][i]: shocks[i,-1] for i in range(len(model.symbols['shocks']))}
start_dict = {model.symbols['shocks'][i]: shocks[i,0] for i in range(len(model.symbols['shocks']))}
start_calib = find_deterministic_equilibrium( model, constraints=start_dict)
final_calib = find_deterministic_equilibrium( model, constraints=start_dict)
start_s = start_calib['states']
start_x = start_calib['controls']
final_s = final_calib['states']
final_x = final_calib['controls']
# if start_constraints:
# ### we ignore start_constraints
# start_dict.update(start_constraints)
# final_equilibrium = start_constraints.copy()
# else:
# final_equilibrium = find_deterministic_equilibrium( model, constraints=final_dict)
# final_s = final_equilibrium['states']
# final_x = final_equilibrium['controls']
# start_s = start_states
# start_x = final_x
#TODO: for start_x, it should be possible to use first order guess
final = numpy.concatenate( [final_s, final_x] )
start = numpy.concatenate( [start_s, start_x] )
if verbose==True:
print("Initial states : {}".format(start_s))
print("Final controls : {}".format(final_x))
p = model.calibration['parameters']
if initial_guess is None:
initial_guess = numpy.row_stack( [start*(1-l) + final*l for l in linspace(0.0,1.0,T+1)] )
else:
from pandas import DataFrame
if isinstance( initial_guess, DataFrame ):
initial_guess = array( initial_guess ).T.copy()
initial_guess = initial_guess[:,:n_s+n_x]
initial_guess[0,:n_s] = start_s
initial_guess[-1,n_s:] = final_x
sh = initial_guess.shape
if model.x_bounds and not ignore_constraints:
initial_states = initial_guess[:,:n_s]
[lb, ub] = [ u( initial_states, p ) for u in model.x_bounds]
lower_bound = initial_guess*0 - numpy.inf
lower_bound[:, n_s:] = lb
upper_bound = initial_guess*0 + numpy.inf
upper_bound[:, n_s:] = ub
test1 = max( lb.max(axis=0) - lb.min(axis=0) )
test2 = max( ub.max(axis=0) - ub.min(axis=0) )
if test1 >0.00000001 or test2>0.00000001:
raise Exception("Not implemented: perfect foresight solution requires that controls have constant bounds.")
else:
ignore_constraints=True
lower_bound = None
upper_bound = None
nn = sh[0]*sh[1]
fobj = lambda vec: det_residual(model, vec.reshape(sh), start_s, final_x, epsilons)[0].ravel()
if not ignore_constraints:
from dolo.numeric.optimize.ncpsolve import ncpsolve
ff = lambda vec: det_residual(model, vec.reshape(sh), start_s, final_x, epsilons, jactype='sparse')
from dolo.numeric.optimize.newton import newton
x0 = initial_guess.ravel()
sol, nit = ncpsolve(ff, lower_bound.ravel(), upper_bound.ravel(), initial_guess.ravel(), verbose=verbose, maxit=maxit, tol=tol, jactype='sparse')
sol = sol.reshape(sh)
else:
from scipy.optimize import root
from numpy import array
# ff = lambda vec: det_residual(model, vec.reshape(sh), start_s, final_x, epsilons, jactype='full')
ff = lambda vec: det_residual(model, vec.reshape(sh), start_s, final_x, epsilons, diff=False).ravel()
x0 = initial_guess.ravel()
sol = root(ff, x0, jac=False)
res = ff(sol.x)
sol = sol.x.reshape(sh)
import pandas
if 'auxiliary' in model.functions:
colnames = model.symbols['states'] + model.symbols['controls'] + model.symbols['auxiliaries']
# compute auxiliaries
y = model.functions['auxiliary'](sol[:,:n_s], sol[:,n_s:], p)
sol = numpy.column_stack([sol,y])
else:
colnames = model.symbols['states'] + model.symbols['controls']
sol = numpy.column_stack([sol,epsilons])
colnames = colnames + model.symbols['shocks']
ts = pandas.DataFrame(sol, columns=colnames)
return ts
e_z = atleast_2d( linspace(0.0, 0.0, 10) ).T
start_s = model.calibration['states'].copy()
start_s[0] = 1.5
import time
#sol1 = deterministic_solve(model, shocks=e_z, T=50, use_pandas=True, ignore_constraints=True, start_s=start_s)
#sol1 = deterministic_solve(model, T=50, use_pandas=True, ignore_constraints=True, start_s=start_s)
from dolo.algos.steady_state import find_deterministic_equilibrium
calib = find_deterministic_equilibrium(model)
t2 = time.time()
sol1 = deterministic_solve(model, start_states=start_s, T=50, use_pandas=True, ignore_constraints=False, verbose=True)
t3 = time.time()
t1 = time.time()
sol2 = deterministic_solve(model, start_states=start_s, T=50, use_pandas=True, ignore_constraints=True, verbose=True)
t2 = time.time()
def approximate_controls(model, verbose=False, steady_state=None, eigmax=1.0, solve_steady_state=False, order=1):
"""Compute first order approximation of optimal controls
Parameters:
-----------
model: NumericModel
Model to be solved
verbose: boolean
If True: displays number of contracting eigenvalues
steady_state: ndarray
Use supplied steady-state value to compute the approximation. The routine doesn't check whether it is really
a solution or not.
solve_steady_state: boolean
Use nonlinear solver to find the steady-state
orders: {1}
Approximation order. (Currently, only first order is supported).
Returns:
--------
TaylorExpansion:
Decision Rule for the optimal controls around the steady-state.
"""
if order > 1:
raise Exception("Not implemented.")
# get steady_state
import numpy
# if model.model_spec == 'fga':
# model = GModel_fg_from_fga(model)
# g = model.functions['transition']
# f = model.functions['arbitrage']
from dolo.algos.dtcscc.convert import get_fg_functions
f = model.functions["arbitrage"]
g = model.functions["transition"]
if steady_state is not None:
calib = steady_state
else:
calib = model.calibration
if solve_steady_state:
from dolo.algos.dtcscc.steady_state import find_deterministic_equilibrium
calib = find_deterministic_equilibrium(model)
p = calib["parameters"]
s = calib["states"]
x = calib["controls"]
e = calib["shocks"]
if model.covariances is not None:
sigma = model.covariances
else:
sigma = numpy.zeros((len(e), len(e)))
from numpy.linalg import solve
l = g(s, x, e, p, diff=True)
[junk, g_s, g_x, g_e] = l[:4] # [el[0,...] for el in l[:4]]
l = f(s, x, e, s, x, p, diff=True)
[res, f_s, f_x, f_e, f_S, f_X] = l # [el[0,...] for el in l[:6]]
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
diag_S = numpy.diag(S)
diag_T = numpy.diag(T)
tol_geneigvals = 1e-10
try:
assert sum((abs(diag_S) < tol_geneigvals) * (abs(diag_T) < tol_geneigvals)) == 0
except Exception as e:
print(e)
# print(numpy.column_stack([diag_S, diag_T]))
raise GeneralizedEigenvaluesError(diag_S, diag_T)
# Check Blanchard=Kahn conditions
n_big_one = sum(eigval > eigmax)
n_expected = n_x
if verbose:
print("There are {} eigenvalues greater than {}. Expected: {}.".format(n_big_one, eigmax, n_x))
if n_expected != n_big_one:
raise BlanchardKahnError(n_big_one, n_expected)
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
def approximate_controls(model, verbose=False, steady_state=None, eigmax=1.0-1e-6,
solve_steady_state=False, order=1):
"""Compute first order approximation of optimal controls
Parameters:
-----------
model: NumericModel
Model to be solved
verbose: boolean
If True: displays number of contracting eigenvalues
steady_state: ndarray
Use supplied steady-state value to compute the approximation.
The routine doesn't check whether it is really a solution or not.
solve_steady_state: boolean
Use nonlinear solver to find the steady-state
orders: {1}
Approximation order. (Currently, only first order is supported).
Returns:
--------
TaylorExpansion:
Decision Rule for the optimal controls around the steady-state.
"""
if order > 1:
raise Exception("Not implemented.")
f = model.functions['arbitrage']
g = model.functions['transition']
if steady_state is not None:
calib = steady_state
else:
calib = model.calibration
if solve_steady_state:
calib = find_deterministic_equilibrium(model)
p = calib['parameters']
s = calib['states']
x = calib['controls']
e = calib['shocks']
distrib = model.get_distribution()
sigma = distrib.sigma
l = g(s, x, e, p, diff=True)
[junk, g_s, g_x, g_e] = l[:4] # [el[0,...] for el in l[:4]]
l = f(s, x, e, s, x, p, diff=True)
[res, f_s, f_x, f_e, f_S, f_X] = l # [el[0,...] for el in l[:6]]
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])
])
[S, T, Q, Z, eigval] = qzordered(A, B, 1.0-1e-8)
Q = Q.real # is it really necessary ?
Z = Z.real
diag_S = np.diag(S)
diag_T = np.diag(T)
tol_geneigvals = 1e-10
try:
ok = sum((abs(diag_S) < tol_geneigvals) *
(abs(diag_T) < tol_geneigvals)) == 0
assert(ok)
except Exception as e:
raise GeneralizedEigenvaluesError(diag_S=diag_S, diag_T=diag_T)
if max(eigval[:n_s]) >= 1 and min(eigval[n_s:]) < 1:
# BK conditions are met
pass
else:
eigval_s = sorted(eigval, reverse=True)
ev_a = eigval_s[n_s-1]
ev_b = eigval_s[n_s]
cutoff = (ev_a - ev_b)/2
if not ev_a > ev_b:
raise GeneralizedEigenvaluesSelectionError(
A=A, B=B, eigval=eigval, cutoff=cutoff,
diag_S=diag_S, diag_T=diag_T, n_states=n_s
)
import warnings
if cutoff > 1:
warnings.warn("Solution is not convergent.")
else:
warnings.warn("There are multiple convergent solutions. The one with the smaller eigenvalues was selected.")
[S, T, Q, Z, eigval] = qzordered(A, B, cutoff)
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
# P = (solve(S11.T, Z11.T).T @ solve(Z11.T, T11.T).T)
C = solve(Z11.T, Z21.T).T
Q = g_e
s = s.ravel()
x = x.ravel()
A = g_s + g_x @ C
B = g_e
dr = CDR([s, x, C])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
def approximate_controls(model,
verbose=False,
steady_state=None,
eigmax=1.0 - 1e-6,
solve_steady_state=False,
order=1):
"""Compute first order approximation of optimal controls
Parameters:
-----------
model: NumericModel
Model to be solved
verbose: boolean
If True: displays number of contracting eigenvalues
steady_state: ndarray
Use supplied steady-state value to compute the approximation.
The routine doesn't check whether it is really a solution or not.
solve_steady_state: boolean
Use nonlinear solver to find the steady-state
orders: {1}
Approximation order. (Currently, only first order is supported).
Returns:
--------
TaylorExpansion:
Decision Rule for the optimal controls around the steady-state.
"""
if order > 1:
raise Exception("Not implemented.")
f = model.functions['arbitrage']
g = model.functions['transition']
if steady_state is not None:
calib = steady_state
else:
calib = model.calibration
if solve_steady_state:
calib = find_deterministic_equilibrium(model)
p = calib['parameters']
s = calib['states']
x = calib['controls']
e = calib['shocks']
distrib = model.get_distribution()
sigma = distrib.sigma
l = g(s, x, e, p, diff=True)
[junk, g_s, g_x, g_e] = l[:4] # [el[0,...] for el in l[:4]]
l = f(s, x, e, s, x, p, diff=True)
[res, f_s, f_x, f_e, f_S, f_X] = l # [el[0,...] for el in l[:6]]
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])])
[S, T, Q, Z, eigval] = qzordered(A, B, 1.0 - 1e-8)
Q = Q.real # is it really necessary ?
Z = Z.real
diag_S = np.diag(S)
diag_T = np.diag(T)
tol_geneigvals = 1e-10
try:
ok = sum((abs(diag_S) < tol_geneigvals) *
(abs(diag_T) < tol_geneigvals)) == 0
assert (ok)
except Exception as e:
raise GeneralizedEigenvaluesError(diag_S=diag_S, diag_T=diag_T)
if max(eigval[:n_s]) >= 1 and min(eigval[n_s:]) < 1:
# BK conditions are met
pass
else:
eigval_s = sorted(eigval, reverse=True)
ev_a = eigval_s[n_s - 1]
ev_b = eigval_s[n_s]
cutoff = (ev_a - ev_b) / 2
if not ev_a > ev_b:
raise GeneralizedEigenvaluesSelectionError(A=A,
B=B,
eigval=eigval,
cutoff=cutoff,
diag_S=diag_S,
diag_T=diag_T,
n_states=n_s)
import warnings
if cutoff > 1:
warnings.warn("Solution is not convergent.")
else:
warnings.warn(
"There are multiple convergent solutions. The one with the smaller eigenvalues was selected."
)
[S, T, Q, Z, eigval] = qzordered(A, B, cutoff)
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
# P = (solve(S11.T, Z11.T).T @ solve(Z11.T, T11.T).T)
C = solve(Z11.T, Z21.T).T
Q = g_e
s = s.ravel()
x = x.ravel()
A = g_s + g_x @ C
B = g_e
dr = CDR([s, x, C])
dr.A = A
dr.B = B
dr.sigma = sigma
return dr
def approximate_controls(model,
verbose=False,
steady_state=None,
eigmax=1.0,
solve_steady_state=False,
order=1):
"""Compute first order approximation of optimal controls
Parameters:
-----------
model: NumericModel
Model to be solved
verbose: boolean
If True: displays number of contracting eigenvalues
steady_state: ndarray
Use supplied steady-state value to compute the approximation.
The routine doesn't check whether it is really a solution or not.
solve_steady_state: boolean
Use nonlinear solver to find the steady-state
orders: {1}
Approximation order. (Currently, only first order is supported).
Returns:
--------
TaylorExpansion:
Decision Rule for the optimal controls around the steady-state.
"""
if order > 1:
raise Exception("Not implemented.")
# get steady_state
# if model.model_spec == 'fga':
# model = GModel_fg_from_fga(model)
f = model.functions['arbitrage']
g = model.functions['transition']
if steady_state is not None:
calib = steady_state
else:
calib = model.calibration
if solve_steady_state:
calib = find_deterministic_equilibrium(model)
p = calib['parameters']
s = calib['states']
x = calib['controls']
e = calib['shocks']
if model.covariances is not None:
sigma = model.covariances
else:
sigma = zeros((len(e), len(e)))
l = g(s, x, e, p, diff=True)
[junk, g_s, g_x, g_e] = l[:4] # [el[0,...] for el in l[:4]]
l = f(s, x, e, s, x, p, diff=True)
[res, f_s, f_x, f_e, f_S, f_X] = l # [el[0,...] for el in l[:6]]
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])])
[S, T, Q, Z, eigval] = qzordered(A, B, n_s)
Q = Q.real # is it really necessary ?
Z = Z.real
diag_S = np.diag(S)
diag_T = np.diag(T)
tol_geneigvals = 1e-10
try:
ok = sum((abs(diag_S) < tol_geneigvals) *
(abs(diag_T) < tol_geneigvals)) == 0
assert (ok)
except Exception as e:
print(e)
# print(np.column_stack([diag_S, diag_T]))
raise GeneralizedEigenvaluesError(diag_S, diag_T)
# Check Blanchard=Kahn conditions
n_big_one = sum(eigval > eigmax)
n_expected = n_x
if verbose:
msg = "There are {} eigenvalues greater than {}. Expected: {}."
print(msg.format(n_big_one, eigmax, n_x))
if n_expected != n_big_one:
raise BlanchardKahnError(n_big_one,
n_expected,
eigval=eigval,
eigmax=eigmax)
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 = 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
