encapsulated Markov-varying parameters in an object

[sbenthall]

There seems to be consensus around the idea that it makes sense to represent continuous #611 and discrete #519 distributions with objects. This has been partially implemented in current code.

Currently, when a parameter varies in a self-contained Markov process that's otherwise exogenous to the model, this is coded with a MrkvArray input and lists for the corresponding parameter values:

https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsPortfolioModel.py#L707-L712

This functionality could be wrapped up into an object.

This is what Dolo does:

https://github.com/EconForge/dolo/blob/7a12eb7117f80d95bd36955328329d6573b0c23c/dolo/numeric/processes.py#L229

[sbenthall]

Integrate this object into library...

[sbenthall]

To integrate this object into the library fully, it requires an expectation-taking function analogous to calcExpectations, see #625

[sbenthall]

Places to use this object:

https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsAggShockModel.py#L965-L980

HARK/HARK/ConsumptionSaving/ConsRepAgentModel.py

Lines 352 to 354 in decc281

	cutoffs = np.cumsum(self.MrkvArray[self.MrkvNow, :])
	MrkvDraw = Uniform(seed=self.RNG.randint(0, 2 ** 31 - 1)).draw(N=1)
	self.MrkvNow = np.searchsorted(cutoffs, MrkvDraw)

maybe this?

HARK/HARK/ConsumptionSaving/ConsAggShockModel.py

Lines 2552 to 2556 in decc281

	draws = Uniform(seed=loops).draw(N=self.act_T_orig)
	for s in range(draws.size): # Add act_T_orig more periods
	MrkvNow_hist[t] = MrkvNow
	MrkvNow = np.searchsorted(cutoffs[MrkvNow, :], draws[s])
	t += 1

[sbenthall]

Related:

HARK/HARK/ConsumptionSaving/ConsMarkovModel.py

Lines 956 to 967 in 2d7b8ab

	def initializeSim(self):
	self.shocks["MrkvNow"] = np.zeros(self.AgentCount, dtype=int)
	IndShockConsumerType.initializeSim(self)
	if (
	self.global_markov
	): # Need to initialize markov state to be the same for all agents
	base_draw = Uniform(seed=self.RNG.randint(0, 2 ** 31 - 1)).draw(1)
	Cutoffs = np.cumsum(np.array(self.MrkvPrbsInit))
	self.shocks["MrkvNow"] = np.ones(self.AgentCount) * np.searchsorted(
	Cutoffs, base_draw
	).astype(int)
	self.shocks["MrkvNow"] = self.shocks["MrkvNow"].astype(int)

HARK/HARK/ConsumptionSaving/ConsMarkovModel.py

Lines 1037 to 1045 in 2d7b8ab

	if (
	not self.global_markov
	): # Markov state is not changed if it is set at the global level
	N = np.sum(which_agents)
	base_draws = Uniform(seed=self.RNG.randint(0, 2 ** 31 - 1)).draw(N)
	Cutoffs = np.cumsum(np.array(self.MrkvPrbsInit))
	self.shocks["MrkvNow"][which_agents] = np.searchsorted(
	Cutoffs, base_draws
	).astype(int)

HARK/HARK/ConsumptionSaving/ConsMarkovModel.py

Lines 1060 to 1096 in 2d7b8ab

	# Draw random numbers that will be used to determine the next Markov state
	if self.global_markov:
	base_draws = np.ones(self.AgentCount) * Uniform(
	seed=self.RNG.randint(0, 2 ** 31 - 1)
	).draw(1)
	else:
	base_draws = Uniform(seed=self.RNG.randint(0, 2 ** 31 - 1)).draw(
	self.AgentCount
	)
	dont_change = (
	self.t_age == 0
	) # Don't change Markov state for those who were just born (unless global_markov)
	if self.t_sim == 0: # Respect initial distribution of Markov states
	dont_change[:] = True
	# Determine which agents are in which states right now
	J = self.MrkvArray[0].shape[0]
	MrkvPrev = self.shocks["MrkvNow"]
	MrkvNow = np.zeros(self.AgentCount, dtype=int)
	MrkvBoolArray = np.zeros((J, self.AgentCount))
	for j in range(J):
	MrkvBoolArray[j, :] = MrkvPrev == j
	# Draw new Markov states for each agent
	for t in range(self.T_cycle):
	Cutoffs = np.cumsum(self.MrkvArray[t], axis=1)
	right_age = self.t_cycle == t
	for j in range(J):
	these = np.logical_and(right_age, MrkvBoolArray[j, :])
	MrkvNow[these] = np.searchsorted(
	Cutoffs[j, :], base_draws[these]
	).astype(int)
	if not self.global_markov:
	MrkvNow[dont_change] = MrkvPrev[dont_change]
	self.shocks["MrkvNow"] = MrkvNow.astype(int)