y = runge(x)
# Plot Runge's Function
fig1 = plt.figure(figsize=[6, 9])
ax1 = fig1.add_subplot(211,
title="Runge's Function",
xlabel='',
ylabel='y',
xticks=[])
ax1.plot(x, y)
ax1.text(-0.8, 0.8, r'$y = \frac{1}{1+25x^2}$', fontsize=18)
# Initialize data matrices
n = np.arange(3, 33, 2)
nn = n.size
errunif, errcheb = (np.zeros([nn, nplot]) for k in range(2))
nrmunif, nrmcheb, conunif, concheb = (np.zeros(nn) for k in range(4))
# Compute approximation errors on refined grid and interpolation matrix condition numbers
for i in range(nn):
# Uniform-node monomial-basis approximant
xnodes = np.linspace(a, b, n[i])
c = np.polyfit(xnodes, runge(xnodes), n[i])
yfit = np.polyval(c, x)
phi = xnodes.reshape(-1, 1)**np.arange(n[i])
errunif[i] = yfit - y
nrmunif[i] = np.log10(norm(yfit - y, np.inf))
conunif[i] = np.log10(cond(phi, 2))
# Chebychev-node Chebychev-basis approximant
__author__ = 'Randall' from demos.setup import np, plt # from quantecon import MarkovChain """ Simulate Simple Markov Chain """ ''' FORMULATION ''' # Model Parameters gamma = 0.07 # aggregate unemployment rate eta = 2.0 # expected duration of unemployment y = [0.51, 1.0] # income per employment state delta = 0.90 # discount factor # Employment Transition Probabilities q = np.zeros([2, 2]) q[0, 0] = 1 - 1 / eta q[1, 0] = gamma * (1 - q[0, 0]) / (1 - gamma) q[0, 1] = 1 - q[0, 0] q[1, 1] = 1 - q[1, 0] # Compute Expected Lifetime Income e = np.linalg.solve(np.identity(2) - delta * q, y) # Compute Stationary Distribution of Employment Expected Employment State Durations # p = MarkovChain(q) # not exactly what markov is in matlab # TODO: Finish this demo!! Markov chain not imported yet from quantecon
S1, S2 = gridmake(s1, s2) # combined state grid
n = n1 * n2 # total number of states
# Action Space (keep='K', replace='R')
X = np.array(['Keep', 'Replace']) # keep or replace
m = X.size # number of actions
# Reward Function
f = np.empty((m, n))
y = (-0.2 * S1**2 + 2 * S1 + 8) * S2 # yield per lactation
f[0] = price * y
f[1] = f[0] - cost
f[0, S1 == 10] = -np.inf # force replace at lactation 10
# State Transition Probability Matrix
P = np.zeros((2, n1, n2, n1, n2))
for i in range(n1):
for j in range(n2):
if i < 9:
P[0, i, j, i + 1, j] = 1 # Raise lactation number by 1, if keep
else:
P[0, i, j, 0] = 0.2, 0.6, 0.2
P[1, i, j, 0] = 0.2, 0.6, 0.2 # Optional replacement
P.shape = 2, n, n
# Model Structure
model = DDPmodel(f, P, delta).solve(print=True)
## Analysis
nplot = 1001
x = np.linspace(a, b, nplot)
y = runge(x)
# Plot Runge's Function
# Initialize data matrices
# In[5]:
n = np.arange(3, 33, 2)
nn = n.size
errunif, errcheb = (np.zeros([nn, nplot]) for k in range(2))
nrmunif, nrmcheb, conunif, concheb = (np.zeros(nn) for k in range(4))
# Compute approximation errors on refined grid and interpolation matrix condition numbers
# In[6]:
for i in range(nn):
# Uniform-node monomial-basis approximant
xnodes = np.linspace(a, b, n[i])
c = np.polyfit(xnodes, runge(xnodes), n[i])
yfit = np.polyval(c, x)
phi = xnodes.reshape(-1, 1) ** np.arange(n[i])
from demos.setup import np, plt # from quantecon import MarkovChain """ Simulate Simple Markov Chain """ ''' FORMULATION ''' # Model Parameters gamma = 0.07 # aggregate unemployment rate eta = 2.0 # expected duration of unemployment y = [0.51, 1.0] # income per employment state delta = 0.90 # discount factor # Employment Transition Probabilities q = np.zeros([2, 2]) q[0, 0] = 1 - 1 / eta q[1, 0] = gamma * (1 - q[0, 0]) / (1 - gamma) q[0, 1] = 1 - q[0, 0] q[1, 1] = 1 - q[1, 0] # Compute Expected Lifetime Income e = np.linalg.solve(np.identity(2) - delta * q, y) # Compute Stationary Distribution of Employment Expected Employment State Durations # p = MarkovChain(q) # not exactly what markov is in matlab # TODO: Finish this demo!! Markov chain not imported yet from quantecon
def f(x):
g = np.zeros((3, x.size))
g[0], g[1], g[2] = np.exp(-x), -np.exp(-x), np.exp(-x)
return g
def f(x):
g = np.zeros((3, x.size))
g[0], g[1], g[2] = np.exp(-x), -np.exp(-x), np.exp(-x)
return g
u = 50 # weekly unemp. benefit v = 60 # weekly value of leisure pfind = 0.90 # prob. of finding job pfire = 0.10 # prob. of being fired delta = 0.99 # discount factor # State Space S = np.array([1, 2]) # vector of states n = S.size # number of states # Action Space (idle=1, active=2) X = ['idle', 'active'] # vector of actions m = len(X) # number of actions # Reward Function f = np.zeros((m, n)) f[0] = v # gets leisure f[1, 0] = u # gets benefit # State Transition Probability Matrix P = np.zeros((m, n, n)) P[0, :, 0] = 1 # remains unemployed P[1, 0, 0] = 1 - pfind # finds no job P[1, 0, 1] = pfind # finds job P[1, 1, 0] = pfire # gets fired P[1, 1, 1] = 1 - pfire # keeps job # Model Structure model = DDPmodel(f, P, delta)
int = F(x, -1)
print('\nThe approximate integral of exp(-x) between x=-1 and x=0 is {:.15f}'.
format(int))
print("The 'exact' integral of exp(-x) between x=-1 and x=0 is {:.15f}".
format(np.exp(1) - 1))
# One may evaluate the accuracy of the Chebychev polynomial approximant by
# computing the approximation error on a highly refined grid of points:
ngrid = 5001 # number of grid nodes
xgrid = np.linspace(a, b, ngrid) # generate refined grid for plotting
yapp = F(xgrid) # approximant values at grid nodes
yact = f(xgrid) # actual function values at grid points
demo.figure('Chebychev Approximation Error for exp(-x)', 'x', 'Error')
plt.plot(xgrid, yapp - yact)
plt.plot(xgrid, np.zeros(ngrid), 'k--', linewidth=2)
# The plot indicates that an order 10 Chebychev approximation scheme, produces approximation errors
# no bigger in magnitude than 6x10^-10. The approximation error exhibits the "Chebychev equioscillation
# property", oscilating relatively uniformly throughout the approximation domain.
#
# This commonly occurs when function being approximated is very smooth, as is the case here but should not
# be expected when the function is not smooth. Further notice how the approximation error is exactly 0 at the
# approximation nodes --- which is true by contruction.
# Let us repeat the approximation exercise, this time constructing a
# 21-function cubic spline approximant:
n = 21 # order of approximation
S = BasisSpline(n, a, b, f=f) # define basis
yapp = S(xgrid) # approximant values at grid nodes
return fval
# In[3]:
A = np.array
as_ = A([9, 3, 18])
bs = A([1, 2, 1])
ad = A([42, 54, 51])
bd = A([3, 2, 1])
c = A([[0, 3, 9], [3, 0, 3], [6, 3, 0]])
params = (as_, bs, ad, bd, c)
# In[4]:
a = np.zeros(9)
b = np.full(9, np.inf)
x0 = np.zeros(9)
Market = MCP(market, a, b, x0, *params)
# In[5]:
x = Market.zero(print=True)
print('Function value at solution\n\t', Market.original(x))
print('Lower bound is binding\n\t', Market.a_is_binding)
print('Upper bound is binding\n\t', Market.b_is_binding)
# print(Market.ssmooth(x))
# print(Market.minmax(x))
# In[6]:
# Model Parameters maxage = 5 # maximum machine age repcost = 75 # replacement cost delta = 0.9 # discount factor # State Space S = np.arange(1, 1 + maxage) # machine age n = S.size # number of states # Action Space (keep=1, replace=2) X = ['keep', 'replace'] # vector of actions m = len(X) # number of actions # Reward Function f = np.zeros((m, n)) f[0] = 50 - 2.5 * S - 2.5 * S**2 f[1] = 50 - repcost f[0, -1] = -np.inf # State Transition Function g = np.zeros_like(f) g[0] = np.arange(1, n + 1) g[0, -1] = n - 1 # adjust last state so it doesn't go out of bounds # Model Structure model = DDPmodel(f, g, delta) model.solve() ## Analysis
__author__ = 'Randall'
# DEMAPP10 Monopolist's Effective Supply Function
# Residual Function
def resid(c):
Q.c = c
q = Q(p)
return p + q / (-3.5 * p**(-4.5)) - np.sqrt(q) - q**2
# Approximation structure
n, a, b = 21, 0.5, 2.5
Q = BasisChebyshev(n, a, b)
c0 = np.zeros(n)
c0[0] = 2
p = Q.nodes
# Solve for effective supply function
monopoly = NLP(resid)
Q.c = monopoly.broyden(c0)
# Setup plot
nplot = 1000
p = nodeunif(nplot, a, b)
rplot = resid(Q.c)
# Plot effective supply
demo.figure("Monopolist's Effective Supply Curve", 'Quantity', 'Price')
plt.plot(Q(p), p)
mancost = 10 # maintenance cost delta = 0.9 # discount factor # State Space s1 = np.arange(1, 1 + maxage) # asset age s2 = s1 - 1 # servicings S = gridmake(s1,s2) # combined state grid S1, S2 = S n = S1.size # total number of states # Action Space X = ['no action', 'service', 'replace'] # vector of actions m = len(X) # number of actions # Reward Function f = np.zeros((m, n)) q = 50 - 2.5 * S1 - 2.5 * S1 ** 2 f[0] = q * np.minimum(1, 1 - (S1 - S2) / maxage) f[1] = q * np.minimum(1, 1 - (S1 - S2 - 1) / maxage) - mancost f[2] = 50 - repcost # State Transition Function g = np.empty_like(f) g[0] = getindex(np.c_[S1 + 1, S2], S) g[1] = getindex(np.c_[S1 + 1, S2 + 1], S) g[2] = getindex(np.c_[1, 0], S) # Model Structure model = DDPmodel(f, g, delta) model.solve()
T = 10 # foraging periods
emax = 8 # energy capacity
e = [2, 4, 4] # energy from foraging
p = [1.0, 0.7, 0.8] # predation survival probabilities
q = [0.5, 0.8, 0.7] # foraging success probabilities
# State Space
S = np.arange(emax + 1) # energy levels
n = S.size # number of states
# Action Space
X = np.arange(3) + 1 # vector of actions
m = X.size # number of actions
# Reward Function
f = np.zeros((m, n))
# State Transition Probability Matrix
P = np.zeros((m, n, n))
for k in range(m):
P[k, 0, 0] = 1
i = range(1, n)
# does not survive predation
snext = 0
j = getindex(snext, S)
P[k, i, j] += 1 - p[k]
# survives predation, finds food
snext = S[i] - 1 + e[k]
j = getindex(snext, S)
P[k, i, j] += p[k] * q[k]
# survives predation, finds no food
# State Space
S = np.arange(1 + maxcap) # vector of states
n = S.size # number of states
# Action Space
X = np.arange(1 + maxcap) # vector of actions
m = X.size # number of actions
# Reward Function
f = np.full((m, n), -np.inf)
for k in range(m):
f[k, k:] = alpha1 * X[k]**beta1 + alpha2 * (S[k:] - X[k])**beta2
# State Transition Probability Matrix
P = np.zeros((m, n, n))
for k in range(m):
for i in range(n):
for j in range(r.size):
snext = min(S[i] - X[k] + r[j], maxcap)
inext = getindex(snext, S)
P[k, i, inext] = P[k, i, inext] + p[j]
# Model Structure
model = DDPmodel(f, P, delta)
model.solve()
## Analysis
# Plot Optimal Policy
print('Interpolation coeff =\n ', c)
# Alternatively, one may compute the standard approximation nodes x and corresponding function values y and use these
# values to create an BasisChebyshev object with keyword argument y:
x = basis.nodes
y = f(x)
fa = BasisChebyshev([n, n], a, b, y=y)
print('Interpolation coeff =\n ', fa.c) # attribute c returns the coefficients
# ... or one may simply pass the function directly to BasisChebyshev using keyword 'f', which by default
# will evaluate it at the basis nodes
F = BasisChebyshev([n, n], a, b, f=f)
print('Interpolation coeff =\n ', F.c)
# Having created a BasisChebyshev object, one may now evaluate the approximant at any point x by calling the object:
x = np.zeros([2, 1]) # first dimension should match the basis dimension
y = F(x)
print('The exact and approximate value of f at x=[0 0] are')
print('{:4.0f} {:20.15f}\n'.format(0, y))
# ... one may also evaluate the approximant's first partial derivatives at x:
d1 = F(x, [1, 0])
d2 = F(x, [0, 1])
print(
'The exact and approximate partial derivatives of f w.r.t. x1 at x=[0 0] are'
)
print('{:4.0f} {:20.15f}\n'.format(1, d1))
print(
'The exact and approximate partial derivatives of f w.r.t. x2 at x=[0 0] are'
)
print('{:4.0f} {:20.15f}\n'.format(0, d2))
# Phi = funbase(basis) # interpolation matrix
## SOLUTION
# Intialize Value Function
# c = zeros(n,1) # conditional value function basis coefficients
# Solve Bellman Equation and Compute Critical Exercise Prices
f = NLP(lambda p: K - np.exp(p) - delta * Value(p))
pcrit = np.empty(N + 1)
pcrit[0] = f.zero(0.0)
for t in range(N):
v = np.zeros((1, n))
for k in range(m):
pnext = Value.nodes + e[k]
v += w[k] * np.maximum(K - np.exp(pnext), delta * Value(pnext))
Value[:] = v
pcrit[t + 1] = f.broyden(pcrit[t])
# Print Critical Exercise Price 300 Periods to Expiration
print('Critical Exercise Price 300 Periods to Expiration')
print(' Critical Price = {:5.2f}'.format(np.exp(pcrit[-1])))
# Plot Critical Exercise Prices
demo.figure('American Put Option Optimal Exercise Boundary',
'Periods Remaining Until Expiration', 'Exercise Price')
u = 50 # weekly unemp. benefit v = 60 # weekly value of leisure pfind = 0.90 # prob. of finding job pfire = 0.10 # prob. of being fired delta = 0.99 # discount factor # State Space S = np.array([1, 2]) # vector of states n = S.size # number of states # Action Space (idle=1, active=2) X = ['idle', 'active'] # vector of actions m = len(X) # number of actions # Reward Function f = np.zeros((m, n)) f[0] = v # gets leisure f[1, 0] = u # gets benefit # State Transition Probability Matrix P = np.zeros((m, n, n)) P[0, :, 0] = 1 # remains unemployed P[1, 0, 0] = 1 - pfind # finds no job P[1, 0, 1] = pfind # finds job P[1, 1, 0] = pfire # gets fired P[1, 1, 1] = 1 - pfire # keeps job # Model Structure model = DDPmodel(f, P, delta) ## Solution
S1, S2 = gridmake(s1,s2) # combined state grid
n = n1 * n2 # total number of states
# Action Space (keep='K', replace='R')
X = np.array(['Keep','Replace']) # keep or replace
m = X.size # number of actions
# Reward Function
f = np.empty((m, n))
y = (-0.2 * S1 ** 2 + 2 * S1 +8) * S2 # yield per lactation
f[0] = price * y
f[1] = f[0] - cost
f[0, S1 == 10] = -np.inf # force replace at lactation 10
# State Transition Probability Matrix
P = np.zeros((2, n1, n2, n1, n2))
for i in range(n1):
for j in range(n2):
if i < 9:
P[0, i, j, i+1, j] = 1 # Raise lactation number by 1, if keep
else:
P[0, i, j, 0] = 0.2, 0.6, 0.2
P[1, i, j, 0] = 0.2, 0.6, 0.2 # Optional replacement
P.shape = 2, n, n
# Model Structure
model = DDPmodel(f, P, delta).solve(print=True)
# Model Parameters
A = 6 # maximum asset age
alpha = np.array([50, -2.5, -2.5]) # production function coefficients
kappa = 40 # net replacement cost
pbar = 1 # long-run mean unit profit
gamma = 0.5 # unit profit autoregression coefficient
sigma = 0.15 # standard deviation of unit profit shock
delta = 0.9 # discount factor
# Continuous State Shock Distribution
m = 5 # number of unit profit shocks
[e,w] = qnwnorm(m,0,sigma ** 2) # unit profit shocks and probabilities
# Deterministic Discrete State Transitions
h = np.zeros((2, A))
h[0, :-1] = np.arange(1, A)
# Approximation Structure
n = 200 # number of collocation nodes
pmin = 0 # minimum unit profit
pmax = 2 # maximum unit profit
basis = BasisSpline(n, pmin, pmax, labels=['unit profit']) # basis functions
# Model Structure
def profit(p, x, i, j):
a = i + 1
if j or a == A:
return p * 50 - kappa
else:
# Model Parameters
u = 90 # unemployment benefit
v = 95 # benefit of pure leisure
wbar = 100 # long-run mean wage
gamma = 0.40 # wage reversion rate
p0 = 0.20 # probability of finding job
p1 = 0.90 # probability of keeping job
sigma = 5 # standard deviation of wage shock
delta = 0.95 # discount factor
# Continuous State Shock Distribution
m = 15 # number of wage shocks
e, w = qnwnorm(m, 0, sigma**2) # wage shocks
# Stochastic Discrete State Transition Probabilities
q = np.zeros((2, 2, 2))
q[1, 0, 1] = p0
q[1, 1, 1] = p1
q[:, :, 0] = 1 - q[:, :, 1]
# Model Structure
# Approximation Structure
n = 150 # number of collocation nodes
wmin = 0 # minimum wage
wmax = 200 # maximum wage
basis = BasisSpline(n, wmin, wmax, labels=['wage']) # basis functions
def reward(w, x, employed, active):
if active:
