Introduction to Dynamic Programming

We have studied the theory of dynamic programming in discrete time under certainty. Let's review what we know so far, so that we can start thinking about how to take to the computer.

The Problem

We want to find a sequence $\{x_t\}_{t=0}^\infty$ and a function $V^*:X\to\mathbb{R}$ such that

$$V^{\ast}\left(x_{0}\right)=\sup\limits _{\left\{ x_{t}\right\} _{t=0}^{\infty}}\sum\limits _{t=0}^{\infty}\beta^{t}U(x_{t},x_{t+1})$$

subject to $x_{t+1}\in G(x_{t})\subseteq X\subseteq\mathbb{R}^K$, for all $t\geq0$ and $x_0\in\mathbb{R}$ given. We assume $\beta\in(0,1)$.

We have seen that we can analyze this problem by solving instead the related problem

$$V(x)=\sup\limits _{y\in G(x)}\left\{ U(x,y)+\beta V(y)\right\} ,\text{ for all }x\in X.$$

Basic Results

Assumptions

  1. $G\left(x\right)$ is nonempty for all $x\in X$ ; and for all $x_{0}\in X$ and $\mathbf{x}\in \Phi (x_{0})$, $\lim\nolimits_{n\rightarrow\infty}\sum_{t=0}^{n}\beta^{t}U(x_{t},x_{t+1})$ exists and is finite.

  2. $X$ is a compact subset of $\mathbb{R}^{K}$, $G$ is nonempty, compact-valued and continuous. Moreover, $U:\mathbf{X}_{G}\rightarrow\mathbb{R}$ is continuous, where $\mathbf{X}_{G}=\left\{ (x,y)\in X\times X:y\in G(x)\right\}$

  3. $U$ is strictly concave and $G$ is convex

  4. For each $y\in X$, $U(\cdot,y)$ is strictly increasing in each of its first $K$ arguments, and $G$ is monotone in the sense that $x\leq x^{\prime}$ implies $G(x)\subset G(x^{\prime})$.

  5. $U$ is continuously differentiable on the interior of its domain $\mathbf{X}_{G}$.

  6. Let $\Phi (x_{t})=\{\{x_{s}\}_{s=t}^{\infty}:x_{s+1}\in G(x_{s})\text{, for }s=t,t+1,...\}$ and assume that $\lim_{t\rightarrow\infty}\beta^{t}V\left(x_{t}\right)=0$ for all $\left(x,x_{1},x_{2},...\right)\in \Phi (x)$.

If all of these conditions are satisfied, then we have the following

Theorem

There exists a unique (value) function $$V^(x_0)=V(x_0),$$ which is continuous, strictly increasing, strictly concave, and differentiable. Also, there exists a unique path ${x^t}{t=0}^\infty$, which starting from the given $x_0$ attains the value $V^(x0)$. The path can be found through a unique continuous policy function $\pi: X\to X$ such that $x^*{t+1}=\pi(x^_t)$.

Taking it to the computer

Ok. Now that we know the conditions for the existence and uniqueness (plus other characteristics) of our problem, how do we go about solving it?

The idea is going to be simple and is based on what we saw when we proved the contraction mapping theorem and the proof of the previous theorem (Yes I know...we split this in various steps and intermediate results, which might have confused you).

Remember that our Bellman Operator $T: C(X)\to C(X)$ defined as

$$T(V(x))\equiv\sup\limits _{y\in G(x)}\left\{ U(x,y)+\beta V(y)\right\}$$

assigns a continuous, strictly increasing, strictly concave function $T(V)$ to each continuous, increasing, and concave function $V$ defined on $X$. Since $T(V)$ is a contraction mapping, we know that if $V_0$ is any initial continuous, increasing, and concave function defined on $X$, then $T^n(V_0)\to V^*$. This is precisely what we are going to do using the computer (well we will also do it by hand for a couple of examples).

Value function iteration

So, now that we have a strategy to tackle the problem, and you have learned some basic Python at Code Academy and IPython in our other notebook, we are ready to write some code and do some dynamic economic analysis.

But before we start, there is one issue I want to highlight. Notice that our state space $X$ is not assumed to be finite, and clearly the fact that our functions are continuous imply that we cannot be in a finite problem. So how do we represent such an infinite object in a computer, which only has finite memory? The solution is to take an approximation to the function, what Stachurski (2009) calls a fitted function. There are various methods to approximate functions (see Judd (1998) for an excellent presentation). The simplest method is a linear interpolation, which is what we will use here.

The idea behind linear interpolation is quite simple. Assume we want to approximate the function $V: X\to X$, $X\subseteq\mathbb{R}$. The only thing we need is a finite set $\{x_i\}_{i=0}^N\subseteq X$ for which we compute the value under $V$, i.e. we create the finite set of values $\{V_i=V(x_i)\}_{i=0}^N$. Then our approximation to the function $V$, $\hat V$, will be defined as

$$\hat V(x)=V_{i-1}+\frac{V_i-V_{i-1}}{x_i-x_{i-1}}(x-x_{i-1}) \quad\text{ if } x_{i-1}\le x < x_i.$$

In principle we could construct our own interpolation function, but Scipy has already optimized approximation algorithms, so let's use that package instead. Let's see what a linear interpolation of $\sin(x)$ would look like.

In [1]:
from __future__ import division
%pylab --no-import-all
%matplotlib inline
from numpy import interp
Using matplotlib backend: MacOSX
Populating the interactive namespace from numpy and matplotlib
In [2]:
interp?
Signature: interp(x, xp, fp, left=None, right=None, period=None)
Docstring:
One-dimensional linear interpolation.

Returns the one-dimensional piecewise linear interpolant to a function
with given discrete data points (`xp`, `fp`), evaluated at `x`.

Parameters
----------
x : array_like
    The x-coordinates at which to evaluate the interpolated values.

xp : 1-D sequence of floats
    The x-coordinates of the data points, must be increasing if argument
    `period` is not specified. Otherwise, `xp` is internally sorted after
    normalizing the periodic boundaries with ``xp = xp % period``.

fp : 1-D sequence of float or complex
    The y-coordinates of the data points, same length as `xp`.

left : optional float or complex corresponding to fp
    Value to return for `x < xp[0]`, default is `fp[0]`.

right : optional float or complex corresponding to fp
    Value to return for `x > xp[-1]`, default is `fp[-1]`.

period : None or float, optional
    A period for the x-coordinates. This parameter allows the proper
    interpolation of angular x-coordinates. Parameters `left` and `right`
    are ignored if `period` is specified.

    .. versionadded:: 1.10.0

Returns
-------
y : float or complex (corresponding to fp) or ndarray
    The interpolated values, same shape as `x`.

Raises
------
ValueError
    If `xp` and `fp` have different length
    If `xp` or `fp` are not 1-D sequences
    If `period == 0`

Notes
-----
The x-coordinate sequence is expected to be increasing, but this is not
explicitly enforced.  However, if the sequence `xp` is non-increasing,
interpolation results are meaningless.

Note that, since NaN is unsortable, `xp` also cannot contain NaNs.

A simple check for `xp` being strictly increasing is::

    np.all(np.diff(xp) > 0)

Examples
--------
>>> xp = [1, 2, 3]
>>> fp = [3, 2, 0]
>>> np.interp(2.5, xp, fp)
1.0
>>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
array([3.  , 3.  , 2.5 , 0.56, 0.  ])
>>> UNDEF = -99.0
>>> np.interp(3.14, xp, fp, right=UNDEF)
-99.0

Plot an interpolant to the sine function:

>>> x = np.linspace(0, 2*np.pi, 10)
>>> y = np.sin(x)
>>> xvals = np.linspace(0, 2*np.pi, 50)
>>> yinterp = np.interp(xvals, x, y)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(xvals, yinterp, '-x')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.show()

Interpolation with periodic x-coordinates:

>>> x = [-180, -170, -185, 185, -10, -5, 0, 365]
>>> xp = [190, -190, 350, -350]
>>> fp = [5, 10, 3, 4]
>>> np.interp(x, xp, fp, period=360)
array([7.5 , 5.  , 8.75, 6.25, 3.  , 3.25, 3.5 , 3.75])

Complex interpolation:

>>> x = [1.5, 4.0]
>>> xp = [2,3,5]
>>> fp = [1.0j, 0, 2+3j]
>>> np.interp(x, xp, fp)
array([0.+1.j , 1.+1.5j])
File:      ~/anaconda3/envs/GeoPython38env/lib/python3.8/site-packages/numpy/lib/function_base.py
Type:      function
In [3]:
x = np.linspace(0, np.pi, 100)
plt.figure(1)
plt.plot(x, np.sin(x), label='Actual Function')
for  i in np.arange(3,11,2):
    fig1 = plt.figure(1)
    xp = np.linspace(0, np.pi, i)
    yp = np.sin(xp)
    y = interp(x, xp, yp)
    plt.plot(x, y, label='Interpolation ' + str(i))
    fig2 = plt.figure(2)
    plt.title('Error with up to ' + str(i) + ' points in interpolation')
    plt.ylabel('Error')
    plt.plot(y - np.sin(x), label=str(i))
    plt.legend(loc=8)
plt.figure(1)
plt.legend(loc=8)
plt.show()
In [4]:
fig1
Out[4]:
In [5]:
fig2
Out[5]:

Clearly the more points we have the better our approximation. But, more points means more computations and more time to get those approximations. Since we will be iterating over approximations, we might not want to use too many points, but be smart about the choice of points or we might want to use less points for a start and then increase the number of points once we have a good candidate solution to our fixed point problem.

In order to make it easy to define interpolated functions, we define a new class of Python object

In [6]:
class LinInterp:
    "Provides linear interpolation in one dimension."

    def __init__(self, X, Y):
        """Parameters: X and Y are sequences or arrays
        containing the (x,y) interpolation points.
        """
        self.X, self.Y = X, Y

    def __call__(self, z):
        """Parameters: z is a number, sequence or array.
        This method makes an instance f of LinInterp callable,
        so f(z) returns the interpolation value(s) at z.
        """
        if isinstance(z, int) or isinstance(z, float):
            return interp ([z], self.X, self.Y)[0]  
        else:
            return interp(z, self.X, self.Y)

We can now define our interpolated sinus function as follows

In [7]:
xp = np.linspace(0, np.pi, 10)
yp = np.sin(xp)
oursin = LinInterp(xp, yp)
plt.plot(oursin(x));

Optimal Growth

Let's start by computing the solution to an optimal growth problem, in which a social planner seeks to find paths $\{c_t,k_t\}$ such that

\begin{align} \max_{\{c_t,k_t\}}&\sum_{t=0}^{\infty}\beta^{t}u(c_{t})\\[.2cm] \text{s.t. }&k_{t+1}\leq f(k_{t})+(1-\delta)k_{t}-c_{t}\\[.2cm] c_{t}\geq0,&\ k_{t}\geq0,\ k_{0}\text{ is given}. \end{align}

As usual we assume that our utility function $u(\cdot)$ and production function $f(\cdot)$ are Neoclassical. Under these conditions we have seen that our problem satisfies the conditions of our previous theorem and thus we know a unique solution exists.

An example with analytical solution

Let's assume that $u(c)=\ln(c)$, $f(k)=k^\alpha$, and $\delta=1$. For this case we have seen that the solution implies

\begin{align} &\text{Value Function: } & V(k)=&\frac{\ln(1-\alpha\beta)}{1-\beta}+\frac{\alpha\beta\ln(\alpha\beta)}{(1-\alpha\beta)(1-\beta)}+\frac{\alpha}{1-\alpha\beta}\ln(k)\\[.2cm] &\text{Optimal Policy: } & \pi\left(k\right)=&\beta\alpha k^{\alpha} \\[.2cm] &\text{Optimal Consumption Function: } & c=&\left(1-\beta\alpha\right)k^{\alpha}\\[.2cm] \end{align}

We will use these to compare the solution found by iteration of the Value function described above. Copy the Python functions you had defined in the previous notebook into the cell below and define Python functions for the actual optimal solutions given above.

In [8]:
%%file optgrowthfuncs.py
def U(c, sigma=1):
    '''This function returns the value of utility when the CRRA
    coefficient is sigma. I.e. 
    u(c,sigma)=(c**(1-sigma)-1)/(1-sigma) if sigma!=1 
    and 
    u(c,sigma)=ln(c) if sigma==1
    Usage: u(c,sigma)
    '''
    if sigma!=1:
        u = (c**(1-sigma)-1) / (1-sigma)
    else:
        u = np.log(c)
    return u

def F(K, L=1, alpha=.3, A=1):
    '''
    Cobb-Douglas production function
    F(K,L)=K^alpha L^(1-alpha)
    '''
    return A * K**alpha * L**(1-alpha)

def Va(k, alpha=.3, beta=.9):
    ab = alpha*beta
    return np.log(1-ab) / (1-beta) + ab * np.log(ab) / ((1-beta) * (1-ab)) + alpha * np.log(k) / (1-ab)

def opk(k, alpha=.3, beta=.9):
    return alpha * beta * k**alpha

def opc(k, alpha=.3, beta=.9):
    return (1-alpha*beta)*k**alpha
Overwriting optgrowthfuncs.py
In [9]:
# %load optgrowthfuncs.py
def U(c, sigma=1):
    '''This function returns the value of utility when the CRRA
    coefficient is sigma. I.e. 
    u(c,sigma)=(c**(1-sigma)-1)/(1-sigma) if sigma!=1 
    and 
    u(c,sigma)=ln(c) if sigma==1
    Usage: u(c,sigma)
    '''
    if sigma!=1:
        u = (c**(1-sigma)-1) / (1-sigma)
    else:
        u = np.log(c)
    return u

def F(K, L=1, alpha=.3, A=1):
    '''
    Cobb-Douglas production function
    F(K,L)=K^alpha L^(1-alpha)
    '''
    return A * K**alpha * L**(1-alpha)

def Va(k, alpha=.3, beta=.9):
    ab = alpha*beta
    return np.log(1-ab) / (1-beta) + ab * np.log(ab) / ((1-beta) * (1-ab)) + alpha * np.log(k) / (1-ab)

def opk(k, alpha=.3, beta=.9):
    return alpha * beta * k**alpha

def opc(k, alpha=.3, beta=.9):
    return (1-alpha*beta)*k**alpha

Let's fix the value of the fundamental parameters so we can realize computations

In [10]:
alpha = .3
beta = .9
sigma = 1
delta = 1

Now let's focus on the Value function iteration:

In [11]:
# Grid of values for state variable over which function will be approximated
gridmin, gridmax, gridsize = 0.1, 5, 300
grid = np.linspace(gridmin, gridmax**1e-1, gridsize)**10

Here we have created a grid on $[gridmin,gridmax]$ that has a number of points given by gridsize. Since we know that the Value functions is stricly concave, our grid has more points closer to zero than farther away

In [12]:
plt.hist(grid, bins=50);
plt.xlabel('State Space');
plt.ylabel('Number of Points');
In [13]:
plt.plot(grid, grid,'r.');
plt.title('State Space Grid');

Now we need a function, which for given $V_0$ solves

$$\sup\limits _{y\in G(x)}\left\{ U(x,y)+\beta V(y)\right\}.$$

Let's use one of Scipy's optimizing routines

In [14]:
from scipy.optimize import fminbound
fminbound?
Signature:
fminbound(
    func,
    x1,
    x2,
    args=(),
    xtol=1e-05,
    maxfun=500,
    full_output=0,
    disp=1,
)
Docstring:
Bounded minimization for scalar functions.

Parameters
----------
func : callable f(x,*args)
    Objective function to be minimized (must accept and return scalars).
x1, x2 : float or array scalar
    The optimization bounds.
args : tuple, optional
    Extra arguments passed to function.
xtol : float, optional
    The convergence tolerance.
maxfun : int, optional
    Maximum number of function evaluations allowed.
full_output : bool, optional
    If True, return optional outputs.
disp : int, optional
    If non-zero, print messages.
        0 : no message printing.
        1 : non-convergence notification messages only.
        2 : print a message on convergence too.
        3 : print iteration results.


Returns
-------
xopt : ndarray
    Parameters (over given interval) which minimize the
    objective function.
fval : number
    The function value at the minimum point.
ierr : int
    An error flag (0 if converged, 1 if maximum number of
    function calls reached).
numfunc : int
  The number of function calls made.

See also
--------
minimize_scalar: Interface to minimization algorithms for scalar
    univariate functions. See the 'Bounded' `method` in particular.

Notes
-----
Finds a local minimizer of the scalar function `func` in the
interval x1 < xopt < x2 using Brent's method.  (See `brent`
for auto-bracketing).

Examples
--------
`fminbound` finds the minimum of the function in the given range.
The following examples illustrate the same

>>> def f(x):
...     return x**2

>>> from scipy import optimize

>>> minimum = optimize.fminbound(f, -1, 2)
>>> minimum
0.0
>>> minimum = optimize.fminbound(f, 1, 2)
>>> minimum
1.0000059608609866
File:      ~/anaconda3/envs/GeoPython38env/lib/python3.8/site-packages/scipy/optimize/optimize.py
Type:      function

Since fminbound returns

$$\arg\min\limits _{y\in [\underline x,\bar x]}\left\{ U(x,y)+\beta V(y)\right\}$$

we have to either replace our objective function for its negative or, better yet, define a function that uses fminbound and returns the maximum and the maximizer

In [15]:
# Maximize function V on interval [a,b]
def maximum(V, a, b, **kwargs):
    return float(V(fminbound(lambda x: -V(x), a, b, **kwargs)))

# Return Maximizer of function V on interval [a,b]
def maximizer(V, a, b, **kwargs):
    return float(fminbound(lambda x: -V(x), a, b, **kwargs))

Note

We could have included other parameters to pass to our maximizer and maximum functions, e.g. to allow us to manipulate the options of fminbound

The Bellman Operator

In [16]:
# The following two functions are used to find the optimal policy and value functions using value function iteration
# Bellman Operator
def bellman(w):
    """The approximate Bellman operator.
    Parameters: w is a LinInterp object (i.e., a 
    callable object which acts pointwise on arrays).
    Returns: An instance of LinInterp that represents the optimal operator.
    w is a function defined on the state space.
    """
    vals = []
    for k in grid:
        kmax = F(k, alpha=alpha) + (1-delta) * k
        h = lambda kp: U(kmax - kp, sigma) + beta * w(kp)
        vals.append(maximum(h, 0, kmax))
    return LinInterp(grid, vals)
In [17]:
# Optimal policy
def policy(w):
    """
    For each function w, policy(w) returns the function that maximizes the 
    RHS of the Bellman operator.
    Replace w for the Value function to get optimal policy.
    The approximate optimal policy operator w-greedy (See Stachurski (2009)). 
    Parameters: w is a LinInterp object (i.e., a 
    callable object which acts pointwise on arrays).
    Returns: An instance of LinInterp that captures the optimal policy.
    """
    vals = []
    for k in grid:
        kmax = F(k,alpha=alpha) + (1-delta) * k
        h = lambda kp: U(kmax - kp,sigma) + beta * w(kp)
        vals.append(maximizer(h, 0, kmax))
    return LinInterp(grid, vals)

Given a linear interpolation of our guess for the Value function, $V_0=w$, the first function returns a LinInterp object, which is the linear interpolation of the function generated by the Bellman Operator on the finite set of points on the grid. The second function returns what Stachurski (2009) calls a w-greedy policy, i.e. the function that maximizes the RHS of the Bellman Operator.

Now we are ready to work on the iteration

In [18]:
# Parameters for the optimization procedures
count = 0
maxiter = 1000
tol = 1e-6
print('tol=%f' % tol)
tol=0.000001

Our initial guess $V_0$

In [19]:
V0 = LinInterp(grid,U(grid))
plt.figure(1)
plt.plot(grid, V0(grid), label='V'+str(count));
plt.plot(grid, Va(grid), label='Actual');
plt.legend(loc=0);

After one interation

In [20]:
plt.plot(grid, V0(grid), label='V'+str(count));
count += 1
V0 = bellman(V0)
plt.figure(1)
plt.plot(grid, V0(grid), label='V'+str(count));
plt.plot(grid, Va(grid), label='Actual');
plt.legend(loc=0);
plt.show();

Doing it by hand is too slow..let's automate this process

In [21]:
fig, ax = plt.subplots()
ax.set_xlim(grid.min(), grid.max())
ax.plot(grid, Va(grid), label='Actual', color='k', lw=2, alpha=0.6);

count=0
maxiter=200
tol=1e-6
while count<maxiter:
    V1 = bellman(V0)
    err = np.max(np.abs(np.array(V1(grid))-np.array(V0(grid))))
    if np.mod(count,10)==0:
        ax.plot(grid, V1(grid), color=plt.cm.jet(count / maxiter), lw=2, alpha=0.6);
        #print('%d %2.10f ' % (count,err))
    V0 = V1
    count += 1
    if err<tol:
        print(count)
        break
ax.plot(grid, V1(grid), label='Estimated', color='r', lw=2, alpha=0.6);
ax.legend(loc='lower right')
plt.draw();
137
In [22]:
fig
Out[22]:

Does it look like we converged? Let's compare our estimated Value function V1 and the actual function Va and compute the error at each point.

In [23]:
print(err)
err = Va(grid)-V1(grid)
plt.plot(grid,err);
print(err.max()-err.min())
5.438395707102472e-07
0.00043285506130530393
In [24]:
fig, ax = plt.subplots()
ax.set_ylim(-10, -7)
ax.set_xlim(grid.min(), grid.max())
ax.plot(grid, Va(grid), label='Actual')
ax.plot(grid, V1(grid)+err.mean(), label='Estimated')
ax.legend(loc='lower right')
plt.show();

Exercise

  1. Use the policy function to compute the optimal policy. Compare it to the actual one
  2. Do the same for the consumption function. Find the savings rate and plot it.
  3. Construct the paths of consumption and capital starting from $k_0=.1$. Show the time series and the paths in the consumption-capital space
  4. Estimate the level of steady state capital and consumption. Show graphically that it is lower than the Golden Rule Level.
  5. Repeat the exercise with other values of $\alpha,\beta,\delta,\sigma,k_0$. Can you write a function or class such that it will generate the whole analysis for given values of the parameters and functions. Can you generalize it in order to analyze the effects of changing the utility or production functions?

Solution

  1. Since we already have V1, we can just apply policy(V1) to get the result
In [25]:
optcapital = policy(V1)
In [26]:
plt.figure(1)
plt.plot(grid, optcapital(grid), label='Estimated Policy Function');
plt.plot(grid, opk(grid), label='Actual Policy Function');
plt.legend(loc='lower right');
In [27]:
err = opk(grid)-optcapital(grid)
plt.plot(grid,err);
print(err.max()-err.min())
print(err.mean())
0.011204400423290684
2.7172657370196723e-05
  1. Since $c = f(k) + (1-\delta) k - k'$
In [28]:
def optcons(k, k1=None):
    '''
    Return value of consumption when capitakl today is k
    '''
    if k1 is None:
        c = F(k) + (1-delta)*k - optcapital(k)
    else:
        c = F(k) + (1-delta)*k - k1
    return c
In [29]:
plt.figure(1)
plt.plot(grid, optcons(grid), label='Estimated Consumption Function');
plt.plot(grid, opc(grid), label='Actual Consumption Function');
plt.legend(loc='lower right');
In [30]:
err = opc(grid)-optcons(grid)
plt.plot(grid,err);
print(err.max()-err.min())
print(err.mean())
0.011204400423290628
-2.7172657370198478e-05

Since $s = f(k) - c = k' - (1-\delta) k = $ investment,

In [31]:
def optsav(k):
    '''
    Estimated savings function
    '''
    s = F(k) - optcons(k)
    return s

def ops(k):
    s = F(k) - opc(k)
    return s
In [32]:
plt.figure(1)
plt.plot(grid, optsav(grid), label='Estimated Savings Function');
plt.plot(grid, ops(grid), label='Actual Savings Function');
plt.legend(loc='lower right');
In [33]:
err = ops(grid)-optsav(grid)
plt.plot(grid,err);
print(err.max()-err.min())
print(err.mean())
0.011204400423290628
2.7172657370198478e-05
  1. Start with $k_0=0.1$ and follow the economy for 20 periods.
In [34]:
T = 20
kt = []
ct = []
k0 = 0.1
kt.append(k0)
for t in range(0,T):
    k1 = optcapital(k0)
    c0 = optcons(k0, k1)
    k0 = k1
    ct.append(c0)
    kt.append(k0)
In [35]:
plt.plot(kt)
plt.xlabel(r'$t$')
plt.ylabel(r'$k_t$')
Out[35]:
Text(0, 0.5, '$k_t$')
In [36]:
plt.plot(ct)
plt.xlabel(r'$t$')
plt.ylabel(r'$c_t$')
Out[36]:
Text(0, 0.5, '$c_t$')
In [37]:
print(kt)
print(ct)
[0.1, 0.13546424543704233, 0.14783790781994552, 0.1543933046617238, 0.15439669348889534, 0.1543966952407572, 0.15439669524166283, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633, 0.1543966952416633]
[0.36572298819023, 0.4011305662298462, 0.4091609622406472, 0.41654076690708036, 0.41654452463206837, 0.4165445265746179, 0.416544526575622, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226, 0.4165445265756226]
  1. Steady state level of capital and consumption are given by these last elements of these lists
In [38]:
css = ct[-1]
kss = kt[-1]
print('Steady state capital is ', kss)
print('Steady state consumption is ', css)
Steady state capital is  0.1543966952416633
Steady state consumption is  0.4165445265756226
In [39]:
k0 = 0.1
maxerr = 1e-14
maxiter = 1000
count = 0
while count<maxiter:
    k1 = optcapital(k0)
    err = np.abs(k1-k0)
    if err<maxerr:
        kss_true = k1
        break
    else:
        k0 = k1
        count += 1
css_true = optcons(kss, kss)
In [40]:
from IPython.display import display, Markdown
display(Markdown(
   rf"""
       True theoretical steady state value of capital {kss_true:1.5}.
       Approximated numerical steady state value of capital {kss:1.5}.
       True theoretical steady state value of consumption {css_true:1.5}.
       Approximated numerical steady state value of consumption {css:1.5}.
       Convergence to steady state in {count} iterations.
   """))
   True theoretical steady state value of capital 0.1544.
   Approximated numerical steady state value of capital 0.1544.
   True theoretical steady state value of consumption 0.41654.
   Approximated numerical steady state value of consumption 0.41654.
   Convergence to steady state in 6 iterations.
In [41]:
print(kss_true)
print(kss)
print(count)
print(css_true)
print(css)
0.1543966952416633
0.1543966952416633
6
0.4165445265756226
0.4165445265756226
In [ ]: