def solveWorkingDeaton(solution_next, aXtraGrid, mGrid, EGMVector, par, Util,
UtilP, UtilP_inv, TranInc, TranIncWeights):
choice = 2
choiceCount = len(solution_next.ChoiceSols)
# Next-period initial wealth given exogenous aXtraGrid
# This needs to be made more general like the rest of the code
mrs_tp1 = par.Rfree * numpy.expand_dims(aXtraGrid,
axis=1) + par.YWork * TranInc.T
mws_tp1 = par.Rfree * numpy.expand_dims(aXtraGrid,
axis=1) + par.YWork * TranInc.T
m_tp1s = (mrs_tp1, mws_tp1)
# Prepare variables for EGM step
C_tp1s = tuple(solution_next.ChoiceSols[d].CFunc(m_tp1s[d])
for d in range(choiceCount))
Vs = tuple(
numpy.divide(-1.0, solution_next.ChoiceSols[d].V_TFunc(m_tp1s[d]))
for d in range(choiceCount))
# Due to the transformation on V being monotonic increasing, we can just as
# well use the transformed values to do this discrete envelope step.
V, ChoiceProb_tp1 = calcLogSumChoiceProbs(numpy.stack(Vs), par.sigma)
# Calculate the expected marginal utility and expected value function
PUtilPsum = sum(ChoiceProb_tp1[i, :] * UtilP(C_tp1s[i], i + 1)
for i in range(choiceCount))
EUtilP_tp1 = par.Rfree * numpy.dot(PUtilPsum, TranIncWeights.T)
EV_tp1 = numpy.squeeze(
numpy.dot(numpy.expand_dims(V, axis=1), TranIncWeights.T))
# EGM step
m_t, C_t, Ev = calcEGMStep(EGMVector, aXtraGrid, EV_tp1, EUtilP_tp1, par,
Util, UtilP, UtilP_inv, choice)
V_T = numpy.divide(-1.0, Util(C_t, choice) + par.DiscFac * Ev)
# We do the envelope step in transformed value space for accuracy. The values
# keep their monotonicity under our transformation.
m_t, C_t, V_T = calcMultilineEnvelope(m_t, C_t, V_T, mGrid)
# The solution is the working specific consumption function and value function
# specifying lower_extrap=True for C is easier than explicitly adding a 0,
# as it'll be linear in the constrained interval anyway.
CFunc = LinearInterp(m_t, C_t, lower_extrap=True)
V_TFunc = LinearInterp(m_t, V_T, lower_extrap=True)
return ChoiceSpecificSolution(m_t, C_t, CFunc, V_T, V_TFunc)
def calcExtraSaves(saveCommon, rs, ws, par, mGrid):
if saveCommon:
# To save the pre-discrete choice expected consumption and value function,
# we need to interpolate onto the same grid between the two. We know that
# ws.C and ws.V_T are on the ws.m grid, so we use that to interpolate.
Crs = LinearInterp(rs.m, rs.C)(mGrid)
V_rs = numpy.divide(-1, LinearInterp(rs.m, rs.V_T)(mGrid))
Cws = ws.C
V_ws = numpy.divide(-1, ws.V_T)
V, P = calcLogSumChoiceProbs(numpy.stack((V_rs, V_ws)), par.sigma)
C = (P * numpy.stack((Crs, Cws))).sum(axis=0)
else:
C, V_T, P = None, None, None
return C, numpy.divide(-1.0, V), P
def test_noShock3DBoth(self):
# Test the value functions and policies of the 3D case
sigma = 0.0
V, P = interpolation.calcLogSumChoiceProbs(self.Vs3D, sigma)
self.assertTrue((V == self.Vref3D).all)
self.assertTrue((P == self.Pref3D).all)
# \end{equation}
#
# \begin{equation}
# c_2(m_2) = c_2(m_2|w=w^*(m_2))
# \end{equation}
#
# We now construct these objects.
# %%
# We use HARK's 'calcLogSumchoiceProbs' to compute the optimal
# will decision over our grid of market resources.
# The function also returns the unconditional value function
# Use transformed values since -given sigma=0- magnitudes are unimportant. This
# avoids NaNs at m \approx 0.
vTGrid2, willChoice2 = calcLogSumChoiceProbs(np.stack(
(vT2_cond_wi(mGrid), vT2_cond_no(mGrid))),
sigma=0)
vGrid2 = vUntransf(vTGrid2)
# Plot the optimal decision rule
plt.plot(mGrid, willChoice2[0])
plt.title('$w^*(m)$')
plt.ylabel('Write will (1) or not (0)')
plt.xlabel('Market resources: m')
plt.show()
# With the decision rule we can get the unconditional consumption function
cGrid2 = (willChoice2 * np.stack(
(c2_cond_wi(mGrid), c2_cond_no(mGrid)))).sum(axis=0)
vT2 = LinearInterp(mGrid, vTransf(vGrid2), lower_extrap=True)
