How we solve a model defined by the
The IndShockConsumerType reprents the work-horse consumption savings model with temporary and permanent shocks to income, finite or infinite horizons, CRRA utility and more. In this DemARK we take you through the steps involved in solving one period of such a model. The inheritance chains can be a little long, so figuring out where all the parameters and methods come from can be a bit confusing. Hence this map! The intention is to make it easier to know how to inheret from IndShockConsumerType
in the sense that you know where to look for specific solver logic, but also so you know can figure out which methods to overwrite or supplement in your own
AgentType and solver! ## The
solveConsIndShock function In HARK, a period’s problem is always solved by the callable (function or callable object instance) stored in the field
solve_one_period. In the case of
IndShockConsumerType, this function is called
solveConsIndShock. The function accepts a number of arguments, that
it uses to construct an instance of either a
ConsIndShockSolverBasic or a
ConsIndShockSolver. These solvers both have the methods
solve, that we will have a closer look at in this notebook. This means, that the logic of
solveConsIndShock is basically:
Check if cubic interpolation (
CubicBool) or construction of the value function interpolant (
vFuncBool) are requested. Construct an instance of
ConsIndShockSolverBasicif neither are requested, else construct a
ConsIndShockSolver. Call this
solver.solve()and return the output as the current solution.
Two types of solvers¶
As mentioned above,
solve_one_period will construct an instance of the class
ConsIndShockSolver. The main difference is whether it uses cubic interpolation or if it explicitly constructs a value function approximation. The choice and construction of a solver instance is bullet 1) from above.
What happens in upon construction¶
Neither of the two solvers have their own
ConsIndShockSolver inherits from
ConsIndShockSolverBasic that in turn inherits from
ConsIndShockSetup inherits from
ConsPerfForesightSolver, which itself is just an
Object, so we get the inheritance structure
When one of the two classes in the end of the inheritance chain is called, it will call
ConsIndShockSetup.__init__(args...). This takes a whole list of fixed inputs that then gets assigned to the object through a
call, that then calls
We’re getting kind of detailed here, but it is simply to help us understand the inheritance structure. The methods are quite straight forward, and simply assign the list of variables to self. The ones that do not get assigned by the
ConsPerfForesightSolver method gets assign by the
ConsIndShockSetup method instead.
After all the input parameters are set, we update the utility function definitions. Remember, that we restrict ourselves to CRRA utility functions, and these are parameterized with the scalar we call
CRRA in HARK. We use the two-argument CRRA utility (and derivatives, inverses, etc) from
HARK.utilities, so we need to create a
lambda (an anonymous function) according to the fixed
CRRA we have chosen. This gets done through a call to
that itself calls
Again, we wish to emphasize the inheritance structure. The method in
ConsPerfForesightSolver defines the most basic utility functions (utility, its marginal and its marginal marginal), and
ConsIndShockSolver adds additional functions (marginal of inverse, inverse of marginal, marginal of inverse of marginal, and optionally inverse if
vFuncBool is true).
To sum up, the
__init__ method lives in
ConsPerfForesightSolver and defines its own methods with the same names that adds some methods used to solve the
IndShockConsumerType using EGM. The main things controlled by the end-user are whether cubic interpolation should be used,
CubicBool, and if the value function should be explicitly formed,
vFuncBool. ### Prepare to solve We are now in bullet 2)
from the list above. The
prepare_to_solve method is all about grabbing relevant information from next period’s solution, calculating some limiting solutions. It comes from
ConsIndShockSetup and calls two methods:
First, we have
setAndUpdateValues. The main purpose is to grab the relevant vectors that represent the shock distributions, the effective discount factor, and value function (marginal, level, marginal marginal depending on the options). It also calculates some limiting marginal propensities to consume and human wealth levels. Second, we have
defBoroCnst. As the name indicates, it calculates the natural borrowing constraint, handles artificial borrowing constraints, and defines the
consumption function where the constraint binds (
prepare_to_solve sets up the stochastic environment an borrowing constraints the consumer might face. It also grabs interpolants from “next period“‘s solution.
The last method
solveConsIndShock will call from the
solve. This method essentially has four steps: 1. Pre-processing for EGM: solver.prepare_to_calc_EndOfPrdvP 1. First step of EGM: solver.calc_EndOfPrdvP 1. Second step of EGM: solver.make_basic_solution 1. Add MPC and human wealth: solver.add_MPC_and_human_wealth
Pre-processing for EGM
Find relevant values of end-of-period asset values (according to
aXtraGrid and natural borrowing constraint) and next period values implied by current period end-of-period assets and stochastic elements. The method stores the following in
values of permanent shocks in
shock probabilities in
next period resources in
current grid of end-of-period assets in
The method also returns
aNrmNow. The definition is in
ConsIndShockSolverBasic and is not overwritten in
First step of EGM
Find the marginal value of having some level of end-of-period assets today. End-of-period assets as well as stochastics imply next-period resources at the beginning of the period, calculated above. Return the result as
Second step of EGM
Apply inverse marginal utility function to nodes from about to find (m, c) pairs for the new consumption function in
get_points_for_interpolation and create the interpolants in
use_points_for_interpolation. The latter constructs the
ConsumerSolution that contains the current consumption function
cFunc, the current marginal value function
vPfunc, and the smallest possible resource level
Add MPC and human wealth
Add values calculated in
defBoroCnst now that we have a solution object to put them in.
Special to the non-Basic solver¶
We are now done, but in the
Basic!) solver there are a few extra steps. We add steady state m, and depending on the values of
CubicBool we also add the value function and the marginal marginal value function.
Let’s try it in action!¶
First, we define a standard lifecycle model, solve it and then
from HARK.ConsumptionSaving.ConsIndShockModel import ( IndShockConsumerType, init_lifecycle, ) import numpy as np import matplotlib.pyplot as plt LifecycleExample = IndShockConsumerType(**init_lifecycle) LifecycleExample.cycles = ( 1 # Make this consumer live a sequence of periods exactly once ) LifecycleExample.solve()
Let’s have a look at the solution in time period second period. We should then be able to
from HARK.utilities import plot_funcs plot_funcs( [LifecycleExample.solution.cFunc], LifecycleExample.solution.mNrmMin, 10 )
Let us then create a solver for the first period.
from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSolverBasic solver = ConsIndShockSolverBasic( LifecycleExample.solution, LifecycleExample.IncShkDstn, LifecycleExample.LivPrb, LifecycleExample.DiscFac, LifecycleExample.CRRA, LifecycleExample.Rfree, LifecycleExample.PermGroFac, LifecycleExample.BoroCnstArt, LifecycleExample.aXtraGrid, LifecycleExample.vFuncBool, LifecycleExample.CubicBool, )
Many important values are now calculated and stored in solver, such as the effective discount factor, the smallest permanent income shock, and more.
These values were calculated in
defBoroCnst that was also called, several things were calculated, for example the consumption function defined by the borrowing constraint.
plot_funcs([solver.cFuncNowCnst], solver.mNrmMinNow, 10)
Then, we set up all the grids, grabs the discrete shock distributions, and state grids in
array([-1.97928710e-01, -1.78757337e-01, -1.58464113e-01, -1.36959776e-01, -1.14146021e-01, -8.99143874e-02, -6.41449810e-02, -3.67050128e-02, -7.44711582e-03, 2.37925970e-02, 5.71967790e-02, 9.29694545e-02, 1.31339050e-01, 1.72561926e-01, 2.16926520e-01, 2.64758197e-01, 3.16424968e-01, 3.72344255e-01, 4.32990903e-01, 4.98906718e-01, 5.70711872e-01, 6.49118576e-01, 7.34947546e-01, 8.29147929e-01, 9.32821508e-01, 1.04725224e+00, 1.17394250e+00, 1.31465773e+00, 1.47148180e+00, 1.64688590e+00, 1.84381499e+00, 2.06579663e+00, 2.31707906e+00, 2.60280747e+00, 2.92925051e+00, 3.30409357e+00, 3.73682117e+00, 4.23921964e+00, 4.82604335e+00, 5.51590530e+00, 6.33247875e+00, 7.30613392e+00, 8.47619016e+00, 9.89404831e+00, 1.16275966e+01, 1.37674827e+01, 1.64361548e+01, 1.98010713e+01])
Then we calculate the marginal utility of next period’s resources given the stochastic environment and current grids.
EndOfPrdvP = solver.calc_EndOfPrdvP()
Then, we essentially just have to construct the (resource, consumption) pairs by completing the EGM step, and constructing the interpolants by using the knowledge that the limiting solutions are those of the perfect foresight model. This is done with
make_basic_solution as discussed above.
solution = solver.make_basic_solution( EndOfPrdvP, solver.aNrmNow, solver.make_linear_cFunc )
Lastly, we add the MPC and human wealth quantities we calculated in the method that prepared the solution of this period.
<HARK.ConsumptionSaving.ConsIndShockModel.ConsumerSolution at 0x177c17f1f40>
All that is left is to verify that the solution in
solution is identical to
LifecycleExample.solution. We can plot the against each other:
plot_funcs( [LifecycleExample.solution.cFunc, solution.cFunc], LifecycleExample.solution.mNrmMin, 10, )
Although, it’s probably even clearer if we just subtract the function values from each other at some grid.
eval_grid = np.linspace(0, 20, 200) LifecycleExample.solution.cFunc(eval_grid) - solution.cFunc(eval_grid)
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