"""
Provides a class called DLE to convert and solve dynamic linear economies
(as set out in Hansen & Sargent (2013)) as LQ problems.
"""
import numpy as np
from ._lqcontrol import LQ
from ._matrix_eqn import solve_discrete_lyapunov
from ._rank_nullspace import nullspace
[docs]class DLE(object):
r"""
This class is for analyzing dynamic linear economies, as set out in Hansen & Sargent (2013).
The planner's problem is to choose \{c_t, s_t, i_t, h_t, k_t, g_t\}_{t=0}^\infty to maximize
\max -(1/2) \mathbb{E} \sum_{t=0}^{\infty} \beta^t [(s_t - b_t).(s_t-b_t) + g_t.g_t]
subject to the linear constraints
\Phi_c c_t + \Phi_g g_t + \Phi_i i_t = \Gamma k_{t-1} + d_t
k_t = \Delta_k k_{t-1} + \Theta_k i_t
h_t = \Delta_h h_{t-1} + \Theta_h c_t
s_t = \Lambda h_{t-1} + \Pi c_t
and
z_{t+1} = A_{22} z_t + C_2 w_{t+1}
b_t = U_b z_t
d_t = U_d z_t
where h_{-1}, k_{-1}, and z_0 are given as initial conditions.
Section 5.5 of HS2013 describes how to map these matrices into those of
a LQ problem.
HS2013 sort the matrices defining the problem into three groups:
Information: A_{22}, C_2, U_b , and U_d characterize the motion of information
sets and of taste and technology shocks
Technology: \Phi_c, \Phi_g, \Phi_i, \Gamma, \Delta_k, and \Theta_k determine the
technology for producing consumption goods
Preferences: \Delta_h, \Theta_h, \Lambda, and \Pi determine the technology for
producing consumption services from consumer goods. A scalar discount factor \beta
determines the preference ordering over consumption services.
Parameters
----------
Information : tuple
Information is a tuple containing the matrices A_{22}, C_2, U_b, and U_d
Technology : tuple
Technology is a tuple containing the matrices \Phi_c, \Phi_g, \Phi_i, \Gamma,
\Delta_k, and \Theta_k
Preferences : tuple
Preferences is a tuple containing the scalar \beta and the
matrices \Lambda, \Pi, \Delta_h, and \Theta_h
"""
def __init__(self, information, technology, preferences):
# === Unpack the tuples which define information, technology and preferences === #
self.a22, self.c2, self.ub, self.ud = information
self.phic, self.phig, self.phii, self.gamma, self.deltak, self.thetak = technology
self.beta, self.llambda, self.pih, self.deltah, self.thetah = preferences
# === Computation of the dimension of the structural parameter matrices === #
self.nb, self.nh = self.llambda.shape
self.nd, self.nc = self.phic.shape
self.nz, self.nw = self.c2.shape
_, self.ng = self.phig.shape
self.nk, self.ni = self.thetak.shape
# === Creation of various useful matrices === #
uc = np.hstack((np.eye(self.nc), np.zeros((self.nc, self.ng))))
ug = np.hstack((np.zeros((self.ng, self.nc)), np.eye(self.ng)))
phiin = np.linalg.inv(np.hstack((self.phic, self.phig)))
phiinc = uc.dot(phiin)
b11 = - self.thetah.dot(phiinc).dot(self.phii)
a1 = self.thetah.dot(phiinc).dot(self.gamma)
a12 = np.vstack((self.thetah.dot(phiinc).dot(
self.ud), np.zeros((self.nk, self.nz))))
# === Creation of the A Matrix for the state transition of the LQ problem === #
a11 = np.vstack((np.hstack((self.deltah, a1)), np.hstack(
(np.zeros((self.nk, self.nh)), self.deltak))))
self.A = np.vstack((np.hstack((a11, a12)), np.hstack(
(np.zeros((self.nz, self.nk + self.nh)), self.a22))))
# === Creation of the B Matrix for the state transition of the LQ problem === #
b1 = np.vstack((b11, self.thetak))
self.B = np.vstack((b1, np.zeros((self.nz, self.ni))))
# === Creation of the C Matrix for the state transition of the LQ problem === #
self.C = np.vstack((np.zeros((self.nk + self.nh, self.nw)), self.c2))
# === Define R,W and Q for the payoff function of the LQ problem === #
self.H = np.hstack((self.llambda, self.pih.dot(uc).dot(phiin).dot(self.gamma), self.pih.dot(
uc).dot(phiin).dot(self.ud) - self.ub, -self.pih.dot(uc).dot(phiin).dot(self.phii)))
self.G = ug.dot(phiin).dot(
np.hstack((np.zeros((self.nd, self.nh)), self.gamma, self.ud, -self.phii)))
self.S = (self.G.T.dot(self.G) + self.H.T.dot(self.H)) / 2
self.nx = self.nh + self.nk + self.nz
self.n = self.ni + self.nh + self.nk + self.nz
self.R = self.S[0:self.nx, 0:self.nx]
self.W = self.S[self.nx:self.n, 0:self.nx]
self.Q = self.S[self.nx:self.n, self.nx:self.n]
# === Use quantecon's LQ code to solve our LQ problem === #
lq = LQ(self.Q, self.R, self.A, self.B,
self.C, N=self.W, beta=self.beta)
self.P, self.F, self.d = lq.stationary_values()
# === Construct output matrices for our economy using the solution to the LQ problem === #
self.A0 = self.A - self.B.dot(self.F)
self.Sh = self.A0[0:self.nh, 0:self.nx]
self.Sk = self.A0[self.nh:self.nh + self.nk, 0:self.nx]
self.Sk1 = np.hstack((np.zeros((self.nk, self.nh)), np.eye(
self.nk), np.zeros((self.nk, self.nz))))
self.Si = -self.F
self.Sd = np.hstack((np.zeros((self.nd, self.nh + self.nk)), self.ud))
self.Sb = np.hstack((np.zeros((self.nb, self.nh + self.nk)), self.ub))
self.Sc = uc.dot(phiin).dot(-self.phii.dot(self.Si) +
self.gamma.dot(self.Sk1) + self.Sd)
self.Sg = ug.dot(phiin).dot(-self.phii.dot(self.Si) +
self.gamma.dot(self.Sk1) + self.Sd)
self.Ss = self.llambda.dot(np.hstack((np.eye(self.nh), np.zeros(
(self.nh, self.nk + self.nz))))) + self.pih.dot(self.Sc)
# === Calculate eigenvalues of A0 === #
self.A110 = self.A0[0:self.nh + self.nk, 0:self.nh + self.nk]
self.endo = np.linalg.eigvals(self.A110)
self.exo = np.linalg.eigvals(self.a22)
# === Construct matrices for Lagrange Multipliers === #
self.Mk = -2 * self.beta.item() * (np.hstack((np.zeros((self.nk, self.nh)), np.eye(
self.nk), np.zeros((self.nk, self.nz))))).dot(self.P).dot(self.A0)
self.Mh = -2 * self.beta.item() * (np.hstack((np.eye(self.nh), np.zeros(
(self.nh, self.nk)), np.zeros((self.nh, self.nz))))).dot(self.P).dot(self.A0)
self.Ms = -(self.Sb - self.Ss)
self.Md = -(np.linalg.inv(np.vstack((self.phic.T, self.phig.T))).dot(
np.vstack((self.thetah.T.dot(self.Mh) + self.pih.T.dot(self.Ms), -self.Sg))))
self.Mc = -(self.thetah.T.dot(self.Mh) + self.pih.T.dot(self.Ms))
self.Mi = -(self.thetak.T.dot(self.Mk))
[docs] def compute_steadystate(self, nnc=2):
"""
Computes the non-stochastic steady-state of the economy.
Parameters
----------
nnc : array_like(float)
nnc is the location of the constant in the state vector x_t
"""
zx = np.eye(self.A0.shape[0])-self.A0
self.zz = nullspace(zx)
self.zz /= self.zz[nnc]
self.css = self.Sc.dot(self.zz)
self.sss = self.Ss.dot(self.zz)
self.iss = self.Si.dot(self.zz)
self.dss = self.Sd.dot(self.zz)
self.bss = self.Sb.dot(self.zz)
self.kss = self.Sk.dot(self.zz)
self.hss = self.Sh.dot(self.zz)
[docs] def compute_sequence(self, x0, ts_length=None, Pay=None):
"""
Simulate quantities and prices for the economy
Parameters
----------
x0 : array_like(float)
The initial state
ts_length : scalar(int)
Length of the simulation
Pay : array_like(float)
Vector to price an asset whose payout is Pay*xt
"""
lq = LQ(self.Q, self.R, self.A, self.B,
self.C, N=self.W, beta=self.beta)
xp, up, wp = lq.compute_sequence(x0, ts_length)
self.h = self.Sh.dot(xp)
self.k = self.Sk.dot(xp)
self.i = self.Si.dot(xp)
self.b = self.Sb.dot(xp)
self.d = self.Sd.dot(xp)
self.c = self.Sc.dot(xp)
self.g = self.Sg.dot(xp)
self.s = self.Ss.dot(xp)
# === Value of J-period risk-free bonds === #
# === See p.145: Equation (7.11.2) === #
e1 = np.zeros((1, self.nc))
e1[0, 0] = 1
self.R1_Price = np.empty((ts_length + 1, 1))
self.R2_Price = np.empty((ts_length + 1, 1))
self.R5_Price = np.empty((ts_length + 1, 1))
for i in range(ts_length + 1):
self.R1_Price[i, 0] = self.beta * e1.dot(self.Mc).dot(np.linalg.matrix_power(
self.A0, 1)).dot(xp[:, i]) / e1.dot(self.Mc).dot(xp[:, i])
self.R2_Price[i, 0] = self.beta**2 * e1.dot(self.Mc).dot(
np.linalg.matrix_power(self.A0, 2)).dot(xp[:, i]) / e1.dot(self.Mc).dot(xp[:, i])
self.R5_Price[i, 0] = self.beta**5 * e1.dot(self.Mc).dot(
np.linalg.matrix_power(self.A0, 5)).dot(xp[:, i]) / e1.dot(self.Mc).dot(xp[:, i])
# === Gross rates of return on 1-period risk-free bonds === #
self.R1_Gross = 1 / self.R1_Price
# === Net rates of return on J-period risk-free bonds === #
# === See p.148: log of gross rate of return, divided by j === #
self.R1_Net = np.log(1 / self.R1_Price) / 1
self.R2_Net = np.log(1 / self.R2_Price) / 2
self.R5_Net = np.log(1 / self.R5_Price) / 5
# === Value of asset whose payout vector is Pay*xt === #
# See p.145: Equation (7.11.1)
if isinstance(Pay, np.ndarray) == True:
self.Za = Pay.T.dot(self.Mc)
self.Q = solve_discrete_lyapunov(
self.A0.T * self.beta**0.5, self.Za)
self.q = self.beta / (1 - self.beta) * \
np.trace(self.C.T.dot(self.Q).dot(self.C))
self.Pay_Price = np.empty((ts_length + 1, 1))
self.Pay_Gross = np.empty((ts_length + 1, 1))
self.Pay_Gross[0, 0] = np.nan
for i in range(ts_length + 1):
self.Pay_Price[i, 0] = (xp[:, i].T.dot(self.Q).dot(
xp[:, i]) + self.q) / e1.dot(self.Mc).dot(xp[:, i])
for i in range(ts_length):
self.Pay_Gross[i + 1, 0] = self.Pay_Price[i + 1,
0] / (self.Pay_Price[i, 0] - Pay.dot(xp[:, i]))
return
[docs] def irf(self, ts_length=100, shock=None):
"""
Create Impulse Response Functions
Parameters
----------
ts_length : scalar(int)
Number of periods to calculate IRF
Shock : array_like(float)
Vector of shocks to calculate IRF to. Default is first element of w
"""
if type(shock) != np.ndarray:
# Default is to select first element of w
shock = np.vstack((np.ones((1, 1)), np.zeros((self.nw - 1, 1))))
self.c_irf = np.empty((ts_length, self.nc))
self.s_irf = np.empty((ts_length, self.nb))
self.i_irf = np.empty((ts_length, self.ni))
self.k_irf = np.empty((ts_length, self.nk))
self.h_irf = np.empty((ts_length, self.nh))
self.g_irf = np.empty((ts_length, self.ng))
self.d_irf = np.empty((ts_length, self.nd))
self.b_irf = np.empty((ts_length, self.nb))
for i in range(ts_length):
self.c_irf[i, :] = self.Sc.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
self.s_irf[i, :] = self.Ss.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
self.i_irf[i, :] = self.Si.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
self.k_irf[i, :] = self.Sk.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
self.h_irf[i, :] = self.Sh.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
self.g_irf[i, :] = self.Sg.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
self.d_irf[i, :] = self.Sd.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
self.b_irf[i, :] = self.Sb.dot(
np.linalg.matrix_power(self.A0, i)).dot(self.C).dot(shock).T
return
[docs] def canonical(self):
"""
Compute canonical preference representation
Uses auxiliary problem of 9.4.2, with the preference shock process reintroduced
Calculates pihat, llambdahat and ubhat for the equivalent canonical household technology
"""
Ac1 = np.hstack((self.deltah, np.zeros((self.nh, self.nz))))
Ac2 = np.hstack((np.zeros((self.nz, self.nh)), self.a22))
Ac = np.vstack((Ac1, Ac2))
Bc = np.vstack((self.thetah, np.zeros((self.nz, self.nc))))
Rc1 = np.hstack((self.llambda.T.dot(self.llambda), -
self.llambda.T.dot(self.ub)))
Rc2 = np.hstack((-self.ub.T.dot(self.llambda), self.ub.T.dot(self.ub)))
Rc = np.vstack((Rc1, Rc2))
Qc = self.pih.T.dot(self.pih)
Nc = np.hstack(
(self.pih.T.dot(self.llambda), -self.pih.T.dot(self.ub)))
lq_aux = LQ(Qc, Rc, Ac, Bc, N=Nc, beta=self.beta)
P1, F1, d1 = lq_aux.stationary_values()
self.F_b = F1[:, 0:self.nh]
self.F_f = F1[:, self.nh:]
self.pihat = np.linalg.cholesky(self.pih.T.dot(
self.pih) + self.beta.dot(self.thetah.T).dot(P1[0:self.nh, 0:self.nh]).dot(self.thetah)).T
self.llambdahat = self.pihat.dot(self.F_b)
self.ubhat = - self.pihat.dot(self.F_f)
return