# OOP in Action: The Samuelson Accelerator

**Co-author: Natasha Watkins**

## Paul Samuelson’s (1939) multiplier-accelerator model

This lecture creates nonstochastic and stochastic versions of Paul
Samuelson’s celebrated multiplier accelerator model, as described in
[[Sam39]](zreferences.ipynb#samuelson1939)

Samuelson used a **second-order linear difference equation** to
represent a model of national output based on three components:

- a **national output identity** asserting that national outcome is the
  sum of output devoted to consumption plus investment plus government
  purchases  
- a Keynesian **consumption function** asserting that consumption at
  time $ t $ is equal to a constant called the **marginal
  propensity to consume** times national output at time $ t-1 $  
- an investment **accelerator** asserting that investment at time
  $ t $ equals a constant called the **accelerator coefficient**
  times the difference in output between period $ t-1 $ and
  $ t-2 $  
- the idea that consumption plus investment plus government purchases
  constitute **aggregate demand** that automatically call forth an
  equal amount of **aggregate supply**  


(To read about linear difference equations see
[here](https://en.wikipedia.org/wiki/Linear_difference_equation) or
chapter IX of [[Sar87]](zreferences.ipynb#sargent1987))

Samuelson used the model to analyze how particular values of the
marginal propensity to consume and the accelerator coefficient might
give rise to transient **business cycles** in national output with
alternative dynamic properties:

- smooth convergence to a constant level of output  
- damped business cycles that eventually converge to a constant level
  of output  
- persistent business cycles that neither dampen nor explode  


Later in the lecture we present an extension to Samuelson’s model that
simply adds a random shock to the right side of the national income
identity (the random shock represents random fluctuations in aggregate demand

This modification makes national output become governed by a second-order
**stochastic linear difference equation** that, with appropriate parameter values,
gives rise to recurrent irregular business cycles.

(To read about stochastic linear difference equations see chapter XI of
[[Sar87]](zreferences.ipynb#sargent1987))

### Structure of the model

In more detail, let’s assume that

- $ \{G_t\} $ is a sequence of levels of government expenditures.
  We’ll start by setting $ G_t = G \forall t $  
- $ \{C_t\} $ is a sequence of levels of aggregate consumption
  expenditures, a key endogenous variable in the model  
- $ \{I_t\} $ is a sequence of rates of investment, another key
  endogenous variable  
- $ \{Y_t\} $ is a sequence of levels of national income, yet
  another endogenous variable  
- $ a $ is the marginal propensity to consume in the Keynesian
  consumption function $ C_t = a Y_{t-1} + \gamma $  
- $ b $ is the “accelerator coefficient” in the “investment
  accerator” $I_t = b (Y_{t-1} - Y_{t-2}) $  
- $ \{\epsilon_{t}\} $ is an IID sequence standard normal random variables  
- $ \sigma \geq 0 $ is a “volatility”
  parameter — setting $ \sigma = 0 $ recovers the nonstochastic case
  that we’ll start with  


The model combines the consumption function


<a id='equation-consumption'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
C_t = a Y_{t-1} + \gamma
$$

</td><td width=25% style='text-align:center !important;'>(1)</td></tr></table>with the investment accelerator


<a id='equation-accelerator'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
I_t = b (Y_{t-1} - Y_{t-2})
$$

</td><td width=25% style='text-align:center !important;'>(2)</td></tr></table>and the national income identity


<a id='equation-income_identity'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_t = C_t + I_t + G_t
$$

</td><td width=25% style='text-align:center !important;'>(3)</td></tr></table>- The parameter $ a $ is peoples’ *marginal propensity to consume*
  out of income - equation [(1)](#equation-consumption) asserts that people consume a fraction of
  math:a in (0,1) of each additional dollar of income  
- The parameter $b > 0 $ is the investment accelerator coefficient - equation
  [(2)](#equation-accelerator) asserts that people invest in physical capital when
  income is increasing and disinvest when it is decreasing  


Equations [(1)](#equation-consumption), [(2)](#equation-accelerator), and [(3)](#equation-income_identity)
imply the following second-order linear difference equation for national income:

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_t = (a+b) Y_{t-1} - b Y_{t-2} + (\gamma + G_t)
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>or


<a id='equation-second_order'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_t = \rho_1 Y_{t-1} + \rho_2 Y_{t-2} + (\gamma + G_t)
$$

</td><td width=25% style='text-align:center !important;'>(4)</td></tr></table>where $ \rho_1 = (a+b) $ and $ \rho_2 = -b $

To complete the model, we require two **initial conditions**

If the model is to generate time series for $ t=0, \ldots, T $, we
require initial values

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_{-1} = \bar Y_{-1}, \quad  Y_{-2} = \bar Y_{-2}
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>We’ll ordinarily set the parameters $ (a,b) $ so that starting from
an arbitrary pair of initial conditions
$ (\bar Y_{-1}, \bar Y_{-2}) $, national income $Y_t $ converges to
a constant value as $ t $ becomes large

We are interested in studying

- the transient fluctuations in $ Y_t $ as it converges to its
  **steady state** level  
- the **rate** at which it converges to a steady state level  


The deterministic version of the model described so far — meaning that
no random shocks hit aggregate demand — has only transient fluctuations

We can convert the model to one that has persistent irregular
fluctuations by adding a random shock to aggregate demand

### Stochastic version of the model

We create a **random** or **stochastic** version of the model by adding
a random process of **shocks** or **disturbances**
$ \{\sigma \epsilon_t \} $ to the right side of equation [(4)](#equation-second_order),
leading to the **second-order scalar linear stochastic difference
equation**:


<a id='equation-second_stochastic'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_t = G_t + a (1-b) Y_{t-1} - a b Y_{t-2} + \sigma \epsilon_{t}
$$

</td><td width=25% style='text-align:center !important;'>(5)</td></tr></table>

### Mathematical analysis of the model

To get started, let’s set $ G_t \equiv 0 $, $ \sigma = 0 $, and
$ \gamma = 0 $.

Then we can write equation [(5)](#equation-second_stochastic) as

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_t = \rho_1 Y_{t-1} + \rho_2 Y_{t-2}
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>or


<a id='equation-second_stochastic2'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_{t+2} - \rho_1 Y_{t+1} - \rho_2 Y_t  = 0
$$

</td><td width=25% style='text-align:center !important;'>(6)</td></tr></table>To discover the properties of the solution of [(6)](#equation-second_stochastic2),
it is useful first to form the **characteristic polynomial**
for [(6)](#equation-second_stochastic2):


<a id='equation-polynomial'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
z^2 - \rho_1 z  - \rho_2
$$

</td><td width=25% style='text-align:center !important;'>(7)</td></tr></table>where $ z $ is possibly a complex number

We want to find the two **zeros** (a.k.a. **roots**) – namely
$ \lambda_1, \lambda_2 $ – of the characteristic polynomial

These are two special values of $ z $, say $ z= \lambda_1 $ and
$ z= \lambda_2 $, such that if we set $ z $ equal to one of
these values in expresssion [(7)](#equation-polynomial),
the characteristic polynomial [(7)](#equation-polynomial) equals zero:


<a id='equation-polynomial_sol'></a>
<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
z^2 - \rho_1 z  - \rho_2  = (z- \lambda_1 ) (z -\lambda_2) = 0
$$

</td><td width=25% style='text-align:center !important;'>(8)</td></tr></table>Equation [(8)](#equation-polynomial_sol) is said to **factor** the characteristic polynomial

When the roots are complex, they will occur as a complex conjugate pair

When the roots are complex, it is convenient to represent them in the
polar form

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
\lambda_1 = r e^{i \theta}, \  \lambda_2 = r e^{-i \theta}
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>where $ r $ is the *amplitude* of the complex number and
$ \theta $ is its *angle*.

These can also be represented as

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
\lambda_1 = r (cos (\theta) + i \sin (\theta))
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table><table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
\lambda_2 = r (cos (\theta) - i \sin(\theta))
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>(To read about the polar form, see
[here](https://www.varsitytutors.com/hotmath/hotmath_help/topics/polar-form-of-a-complex-number))

Given **initial conditions** $ Y_{-1}, Y_{-2} $ we want to generate
a **solution** of the difference equation [(6)](#equation-second_stochastic2)

It can be represented as

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_t = \lambda_1^t c_1 + \lambda_2^t c_2
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>where $ c_1 $ and $ c_2 $ are constants that depend on the two
initial conditions and on $ \rho_1, \rho_2 $

When the roots are complex, some algebra that exploits the fact that the
roots appear as a complex conjugate pair implies that

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
Y_t = \tilde c_1 r^t \cos(\theta t + \tilde c_1)
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>where $ \tilde c_1, \tilde c_2 $ is a pair of constants chosen to
satisfy the given initial conditions for $ Y_{-1}, Y_{-2} $

This formula shows that when the roots are complex, $ Y_t $ displays
oscillations with **period** $ \check p =
\frac{2 \pi}{\theta} $ and **damping factor** $ r $

**Remark:** Following [[Sam39]](zreferences.ipynb#samuelson1939), we want to choose the parameters
$ a, b $ of the model so that the absolute values (of the possibly
complex) roots $ \lambda_1, \lambda_2 $ of the characteristic
polynomial are both strictly less than one:

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
| \lambda_j | < 1 \quad \quad \text{for } j = 1, 2
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>**Remark:** When both eigenvalues $ \lambda_1, \lambda_2 $ have
absolute values strictly less than one, the absolute value of the larger
one governs the rate of convergence to the steady state of the non
stochastic version of the model

### Things this lecture does

1. Writes a function to generate simulations of a $ \{Y_t\} $
   sequence as a function of time. Say about 40 or 80 periods. The
   function checks that $ a, b $ are set so that
   $ \lambda\_1, \lambda\_2 $ are less than
   unity in absolute value (also called “modulus”). The function also
   tells us whether the roots are complex, and, if they are complex,
   returns both their real and complex parts; if the roots are both
   real, the function returns their values. The function requires that
   we put in initial conditions for $ Y_{-1}, Y_{-2} $  
1. The lecture produces a version of a graph in chapter IX, p. 189, of
   [[Sar87]](zreferences.ipynb#sargent1987). The
   graph maps values of $ \rho_1, \rho_2 $ into various regions –
   including ones that produce complex roots and therefore “cycles” in
   the outputs. We can use versions of this graph to help us guide how
   to set $ a,b $  
1. We use our function written to simulate paths that are stochastic
   (when $ \sigma >0 $)  
1. We have written the function in a way that allows us to input
   $ \{G_t\} $ paths of a few simple forms – e.g., one time jumps
   in $ G $ at some time; and a permanent jump in $ G $ that
   occurs at some time  
1. We proceed to use the Samuelson multiplier-accererator model as a
   laboratory to make a simple OOP example that eventually can follow
   the example of the Solow model class in [this lecture](https://lectures.quantecon.org/py/python_oop.html).
   One lesson from
   doing this is that in writing the function and class to simulate the
   model, the “state” that determines next period’s $ Y_{t+1} $ is
   now not just the current value $ Y_t $ but also the once lagged
   value $ Y_{t-1} $. This involves a little more bookkeeping than is
   required in the Solow model class definition  
1. We use the Samuelson multiplier-accelerator model as a vehicle for
   teaching how we can gradually add more and more fun features to the
   class. We want to have a method in the class that automatically
   generates a simulation, either nonstochastic ($ \sigma=0 $) or
   stochastic ($ \sigma > 0 $)  
1. We also show how to map the Samuelson model into a simple instance of
   the LinearStateSpace class described [here](https://lectures.quantecon.org/py/linear_models.html).
   We can use a LinearStateSpace instance to do various things that we did above with
   our homemade function and class. Among other things, we show by
   example that the eigenvalues of the matrix $ A $ that we use to
   form the instance of the LinearStateSpace class for the Samuelson
   model equal the roots of the characteristic polynomial [(7)](#equation-polynomial) for the
   Samuelson multiplier accelerator model. Here is the formula for the
   matrix $ A $ in the linear state space system in the case that
   government expenditures are a constant $ G $:  


<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
A = \begin{bmatrix} 1 & 0 & 0 \cr
                      \gamma + G & \rho_1 & \rho_2 \cr
                      0 & 1 & 0 \end{bmatrix}
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>

## Let’s get to work

We’ll start by drawing an informative graph from page 189 of [[Sar87]](zreferences.ipynb#sargent1987)


```python
import numpy as np
import matplotlib.pyplot as plt

def param_plot():

    """this function creates the graph on page 189 of Sargent Macroeconomic Theory, second edition, 1987"""

    fig, ax = plt.subplots(figsize=(12, 8))
    ax.set_aspect('equal')

    # Set axis
    xmin, ymin = -3, -2
    xmax, ymax = -xmin, -ymin
    plt.axis([xmin, xmax, ymin, ymax])

    # Set axis labels
    ax.set(xticks=[], yticks=[])
    ax.set_xlabel(r'$\rho_2$', fontsize=16)
    ax.xaxis.set_label_position('top')
    ax.set_ylabel(r'$\rho_1$', rotation=0, fontsize=16)
    ax.yaxis.set_label_position('right')

    # Draw (t1, t2) points
    rho1 = np.linspace(-2, 2, 100)
    ax.plot(rho1, -abs(rho1) + 1, c='black')
    ax.plot(rho1, np.ones_like(rho1) * -1, c='black')
    ax.plot(rho1, -(rho1**2 / 4), c='black')

    # Turn normal axes off
    for spine in ['left', 'bottom', 'top', 'right']:
        ax.spines[spine].set_visible(False)

    # Add arrows to represent axes
    axes_arrows = {'arrowstyle': '<|-|>', 'lw': 1.3}
    ax.annotate('', xy=(xmin, 0), xytext=(xmax, 0), arrowprops=axes_arrows)
    ax.annotate('', xy=(0, ymin), xytext=(0, ymax), arrowprops=axes_arrows)

    # Annotate the plot with equations
    plot_arrowsl = {'arrowstyle': '-|>', 'connectionstyle': "arc3, rad=-0.2"}
    plot_arrowsr = {'arrowstyle': '-|>', 'connectionstyle': "arc3, rad=0.2"}
    ax.annotate(r'$\rho_1 + \rho_2 < 1$', xy=(0.5, 0.3), xytext=(0.8, 0.6),
                arrowprops=plot_arrowsr, fontsize='12')
    ax.annotate(r'$\rho_1 + \rho_2 = 1$', xy=(0.38, 0.6), xytext=(0.6, 0.8),
                arrowprops=plot_arrowsr, fontsize='12')
    ax.annotate(r'$\rho_2 < 1 + \rho_1$', xy=(-0.5, 0.3), xytext=(-1.3, 0.6),
                arrowprops=plot_arrowsl, fontsize='12')
    ax.annotate(r'$\rho_2 = 1 + \rho_1$', xy=(-0.38, 0.6), xytext=(-1, 0.8),
                arrowprops=plot_arrowsl, fontsize='12')
    ax.annotate(r'$\rho_2 = -1$', xy=(1.5, -1), xytext=(1.8, -1.3),
                arrowprops=plot_arrowsl, fontsize='12')
    ax.annotate(r'${\rho_1}^2 + 4\rho_2 = 0$', xy=(1.15, -0.35),
                xytext=(1.5, -0.3), arrowprops=plot_arrowsr, fontsize='12')
    ax.annotate(r'${\rho_1}^2 + 4\rho_2 < 0$', xy=(1.4, -0.7),
                xytext=(1.8, -0.6), arrowprops=plot_arrowsr, fontsize='12')

    # Label categories of solutions
    ax.text(1.5, 1, 'Explosive\n growth', ha='center', fontsize=16)
    ax.text(-1.5, 1, 'Explosive\n oscillations', ha='center', fontsize=16)
    ax.text(0.05, -1.5, 'Explosive oscillations', ha='center', fontsize=16)
    ax.text(0.09, -0.5, 'Damped oscillations', ha='center', fontsize=16)

    # Add small marker to y-axis
    ax.axhline(y=1.005, xmin=0.495, xmax=0.505, c='black')
    ax.text(-0.12, -1.12, '-1', fontsize=10)
    ax.text(-0.12, 0.98, '1', fontsize=10)

    return fig

param_plot()
plt.show()
```

  

### Explanation of the graph

The graph portrays regions in which the $ (\lambda_1, \lambda_2) $
root pairs implied by the $ (\rho_1 = (a+b), \rho_2 = - b) $
difference equation parameter pairs in the Samuelson model are such that:

- $ (\lambda_1, \lambda_2) $ are complex with modulus less than
  $ 1 $ - in this case, the $ \{Y_t\} $ sequence displays damped
  oscillations  
- $ (\lambda_1, \lambda_2) $ are both real, but one is strictly
  greater than $ 1 $ - this leads to explosive growth  
- $ (\lambda_1, \lambda_2) $ are both real, but one is strictly
  less than $ -1 $ - this leads to explosive oscillations  
- $ (\lambda_1, \lambda_2) $ are both real and both are less than
  $ 1 $ in absolute value - in this case, there is smooth
  convergence to the steady state without damped cycles  


Later we’ll present the graph with a red mark showing the particular
point implied by the setting of $ (a,b) $

### Function to describe implications of characteristic polynomial


```python
def categorize_solution(rho1, rho2):
    """this function takes values of rho1 and rho2 and uses them to classify the type of solution"""

    discriminant = rho1 ** 2 + 4 * rho2
    if rho2 > 1 + rho1 or rho2 < -1:
        print('Explosive oscillations')
    elif rho1 + rho2 > 1:
        print('Explosive growth')
    elif discriminant < 0:
        print('Roots are complex with modulus less than one; therefore damped oscillations')
    else:
        print('Roots are real and absolute values are less than zero; therfore get smooth convergence to a steady state')
```


```python
### Test the categorize_solution function

categorize_solution(1.3, -.4)
```

    Roots are real and absolute values are less than zero; therfore get smooth convergence to a steady state


```none
Roots are real and absolute values are less than zero; therfore get smooth convergence to a steady state
```


### Function for plotting $ Y_t $ paths

A useful function for our work below


```python
def plot_y(function=None):
    """function plots path of Y_t"""
    plt.subplots(figsize=(12, 8))
    plt.plot(function)
    plt.xlabel('Time $t$')
    plt.ylabel('$Y_t$', rotation=0)
    plt.grid()
    plt.show()
```

### Manual or “by hand” root calculations

The following function calculates roots of the characteristic polynomial
using high school algebra

(We’ll calculate the roots in other ways later)

The function also plots a $ Y_t $ starting from initial conditions
that we set


```python
from cmath import sqrt

##=== This is a 'manual' method ===#

def y_nonstochastic(y_0=100, y_1=80, alpha=.92, beta=.5, gamma=10, n=80):

    """Takes values of parameters and computes roots of characteristic polynomial.
       It tells whether they are real or complex and whether they are less than unity in absolute value.
       It also computes a simulation of length n starting from the two given initial conditions for national income"""

    roots = []

    rho1 = alpha + beta
    rho2 = -beta

    print('rho_1 is ', rho1)
    print('rho_2 is ', rho2)

    discriminant = rho1 ** 2 + 4 * rho2

    if discriminant == 0:
        roots.append(-rho1 / 2)
        print('Single real root: ')
        print(''.join(str(roots)))
    elif discriminant > 0:
        roots.append((-rho1 + sqrt(discriminant).real) / 2)
        roots.append((-rho1 - sqrt(discriminant).real) / 2)
        print('Two real roots: ')
        print(''.join(str(roots)))
    else:
        roots.append((-rho1 + sqrt(discriminant)) / 2)
        roots.append((-rho1 - sqrt(discriminant)) / 2)
        print('Two complex roots: ')
        print(''.join(str(roots)))

    if all(abs(root) < 1 for root in roots):
        print('Absolute values of roots are less than one')
    else:
        print('Absolute values of roots are not less than one')

    def transition(x, t): return rho1 * x[t - 1] + rho2 * x[t - 2] + gamma

    y_t = [y_0, y_1]

    for t in range(2, n):
        y_t.append(transition(y_t, t))

    return y_t

plot_y(y_nonstochastic())
```

```none
rho_1 is  1.42
rho_2 is  -0.5
Two real roots:
[-0.6459687576256715, -0.7740312423743284]
Absolute values of roots are less than one
```


  

### Reverse engineering parameters to generate damped cycles

The next cell writes code that takes as inputs the modulus $ r $ and
phase $ \phi $ of a conjugate pair of complex numbers in polar form

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
\lambda_1 = r \exp(i \phi), \quad \lambda_2 = r \exp(- i \phi)
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>- The code assumes that these two complex numbers are the roots of the
  characteristic polynomial  
- It then reverse engineers $ (a,b) $ and $ (\rho_1, \rho_2) $,
  pairs that would generate those roots  


```python
### code to reverse engineer a  cycle
### y_t = r^t (c_1 cos(phi t) + c2 sin(phi t))
###

import cmath
import math

def f(r, phi):
    """
    Takes modulus r and angle phi of complex number r exp(j phi)
    and creates rho1 and rho2 of characteristic polynomial for which
    r exp(j phi) and r exp(- j phi) are complex roots.

    Returns the multiplier coefficient a and the accelerator coefficient b
    that verifies those roots.
    """
    g1 = cmath.rect(r, phi)  # Generate two complex roots
    g2 = cmath.rect(r, -phi)
    rho1 = g1 + g2           # Implied rho1, rho2
    rho2 = -g1 * g2
    b = -rho2                # Reverse engineer a and b that validate these
    a = rho1 - b
    return rho1, rho2, a, b

## Now let's use the function in an example
## Here are the example paramters

r = .95
period = 10   # Length of cycle in units of time
phi = 2 * math.pi/period

## Apply the function

rho1, rho2, a, b = f(r, phi)

print("a, b = ", a, b)
print("rho1, rho2 =", rho1, rho2)
```

    a, b =  (0.6346322893124001+0j) (0.9024999999999999-0j)
    rho1, rho2 = (1.5371322893124+0j) (-0.9024999999999999+0j)


```none
a, b =  (0.6346322893124001+0j) (0.9024999999999999-0j)
rho1, rho2 = (1.5371322893124+0j) (-0.9024999999999999+0j)
```



```python
## Print the real components of rho1 and rho2

rho1 = rho1.real
rho2 = rho2.real

rho1, rho2
```

```none
(1.5371322893124, -0.9024999999999999)
```


### Root finding using numpy

Here we’ll use numpy to compute the roots of the characteristic
polynomial


```python
r1, r2 = np.roots([1, -rho1, -rho2])

p1 = cmath.polar(r1)
p2 = cmath.polar(r2)

print("r, phi =", r, phi)
print("p1, p2 = ", p1, p2)
# print("g1, g2 = ", g1, g2)

print("a, b =", a, b)
print("rho1, rho2 =", rho1, rho2)
```

    r, phi = 0.95 0.6283185307179586
    p1, p2 =  (0.95, 0.6283185307179586) (0.95, -0.6283185307179586)
    a, b = (0.6346322893124001+0j) (0.9024999999999999-0j)
    rho1, rho2 = 1.5371322893124 -0.9024999999999999


```none
r, phi = 0.95 0.6283185307179586
p1, p2 =  (0.95, 0.6283185307179586) (0.95, -0.6283185307179586)
a, b = (0.6346322893124001+0j) (0.9024999999999999-0j)
rho1, rho2 = 1.5371322893124 -0.9024999999999999
```



```python
##=== This method uses numpy to calculate roots ===#


def y_nonstochastic(y_0=100, y_1=80, alpha=.9, beta=.8, gamma=10, n=80):

    """ Rather than computing the roots of the characteristic polynomial by hand as we did earlier, this function
    enlists numpy to do the work for us """

    # Useful constants
    rho1 = alpha + beta
    rho2 = -beta

    categorize_solution(rho1, rho2)

    # Find roots of polynomial
    roots = np.roots([1, -rho1, -rho2])
    print('Roots are', roots)

    # Check if real or complex
    if all(isinstance(root, complex) for root in roots):
        print('Roots are complex')
    else:
        print('Roots are real')

    # Check if roots are less than one
    if all(abs(root) < 1 for root in roots):
        print('Roots are less than one')
    else:
        print('Roots are not less than one')

    # Define transition equation
    def transition(x, t): return rho1 * x[t - 1] + rho2 * x[t - 2] + gamma

    # Set initial conditions
    y_t = [y_0, y_1]

    # Generate y_t series
    for t in range(2, n):
        y_t.append(transition(y_t, t))

    return y_t

plot_y(y_nonstochastic())
```

```none
Roots are complex with modulus less than one; therefore damped oscillations
Roots are [ 0.85+0.27838822j  0.85-0.27838822j]
Roots are complex
Roots are less than one
```


  

### Reverse engineered complex roots: example

The next cell studies the implications of revese engineered complex
roots

We’ll generate an **undamped** cycle of period 10


```python
r = 1   # generates undamped, nonexplosive cycles

period = 10   #  length of cycle in units of time
phi = 2 * math.pi/period

## Apply the reverse engineering function f

rho1, rho2, a, b = f(r, phi)

a = a.real  # drop the imaginary part so that it is a valid input into y_nonstochastic
b = b.real

print("a, b =", a, b)

ytemp = y_nonstochastic(alpha=a, beta=b, y_0=20, y_1=30)
plot_y(ytemp)
```

```none
a, b = 0.6180339887498949 1.0
Roots are complex with modulus less than one; therefore damped oscillations
Roots are [ 0.80901699+0.58778525j  0.80901699-0.58778525j]
Roots are complex
Roots are less than one
```


  

### Interactive demonstration

We'll use some widgets to show some of the consequences of parameters $(a,b)$ that are associated with complex conjugate roots of the characteristic polynomial. The widget will allow us to specify the amplitude and angle of the complex roots of the characteristic polynomial. (It will reverse engineer the $(a,b)$ that imply those roots.)


```python
from ipywidgets import interact

def choose_r_phi(r, phi):
    
    rho1, rho2, a, b = f(r, phi)
    a = a.real  # drop the imaginary part so that it is a valid input into y_nonstochastic
    b = b.real
    ytemp = y_nonstochastic(alpha=a, beta=b, y_0=20, y_1=30)
    
    return plot_y(ytemp)
```


```python
periodhighest = 20

interact(choose_r_phi, r=(0, 1.5), phi=(math.pi/periodhighest, 10 * math.pi/periodhighest))
```


    A Jupyter Widget





    <function __main__.choose_r_phi>



### Digression: using sympy to find roots

We can also use sympy to compute analytic formulas for the roots


```python
import sympy
from sympy import Symbol, init_printing
init_printing()

r1 = Symbol("rho_1")
r2 = Symbol("rho_2")
z = Symbol("z")

sympy.solve(z**2 - r1*z - r2, z)
```

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
\left [ \frac{\rho_{1}}{2} - \frac{1}{2} \sqrt{\rho_{1}^{2} + 4 \rho_{2}},
\quad \frac{\rho_{1}}{2} + \frac{1}{2} \sqrt{\rho_{1}^{2} + 4 \rho_{2}}\right ]
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>


```python
a = Symbol("alpha")
b = Symbol("beta")
r1 = a + b
r2 = -b

sympy.solve(z**2 - r1*z - r2, z)
```

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
\left [ \frac{\alpha}{2} + \frac{\beta}{2} - \frac{1}{2} \sqrt{\alpha^{2} +
2 \alpha \beta + \beta^{2} - 4 \beta}, \quad \frac{\alpha}{2} +
\frac{\beta}{2} + \frac{1}{2} \sqrt{\alpha^{2} + 2 \alpha \beta +
\beta^{2} - 4 \beta}\right ]
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>

## Stochastic shocks

Now we’ll construct some code to simulate the stochastic version of the
model that emerges when we add a random shock process to aggregate
demand


```python
def y_stochastic(y_0=0, y_1=0, alpha=0.8, beta=0.2, gamma=10, n=100, sigma=5):

    """This function takes parameters of a stochastic version of the model and proceeds to analyze
    the roots of the characteristic polynomial and also generate a simulation"""

    # Useful constants
    rho1 = alpha + beta
    rho2 = -beta

    # Categorize solution
    categorize_solution(rho1, rho2)

    # Find roots of polynomial
    roots = np.roots([1, -rho1, -rho2])
    print(roots)

    # Check if real or complex
    if all(isinstance(root, complex) for root in roots):
        print('Roots are complex')
    else:
        print('Roots are real')

    # Check if roots are less than one
    if all(abs(root) < 1 for root in roots):
        print('Roots are less than one')
    else:
        print('Roots are not less than one')

    # Generate shocks
    epsilon = np.random.normal(0, 1, n)

    # Define transition equation
    def transition(x, t): return rho1 * \
        x[t - 1] + rho2 * x[t - 2] + gamma + sigma * epsilon[t]

    # Set initial conditions
    y_t = [y_0, y_1]

    # Generate y_t series
    for t in range(2, n):
        y_t.append(transition(y_t, t))

    return y_t

plot_y(y_stochastic())
```

```none
Roots are real and absolute values are less than zero; therfore get smooth convergence to a steady state
[ 0.7236068  0.2763932]
Roots are real
Roots are less than one
```


  
Let’s do a simulation in which there are shocks and the characteristic polynomial has complex roots


```python
r = .97

period = 10   #  length of cycle in units of time
phi = 2 * math.pi/period

### apply the  reverse engineering function f

rho1, rho2, a, b = f(r, phi)

a = a.real  # drop the imaginary part so that it is a valid input into y_nonstochastic
b = b.real

print("a, b = ", a, b)
plot_y(y_stochastic(y_0=40, y_1 = 42, alpha=a, beta=b, sigma=2, n=100))
```

```none
a, b =  0.6285929690873979 0.9409000000000001
Roots are complex with modulus less than one; therefore damped oscillations
[ 0.78474648+0.57015169j  0.78474648-0.57015169j]
Roots are complex
Roots are less than one
```


  

## Government spending

This function computes a response to either a permanent or one-off increase
in government expenditures


```python
def y_stochastic_g(y_0=20,
                   y_1=20,
                   alpha=0.8,
                   beta=0.2,
                   gamma=10,
                   n=100,
                   sigma=2,
                   g=0,
                   g_t=0,
                   duration='permanent'):

    """This program computes a response to a permanent increase in government expenditures that occurs
       at time 20"""

    # Useful constants
    rho1 = alpha + beta
    rho2 = -beta

    # Categorize solution
    categorize_solution(rho1, rho2)

    # Find roots of polynomial
    roots = np.roots([1, -rho1, -rho2])
    print(roots)

    # Check if real or complex
    if all(isinstance(root, complex) for root in roots):
        print('Roots are complex')
    else:
        print('Roots are real')

    # Check if roots are less than one
    if all(abs(root) < 1 for root in roots):
        print('Roots are less than one')
    else:
        print('Roots are not less than one')

    # Generate shocks
    epsilon = np.random.normal(0, 1, n)

    def transition(x, t, g):

        # Non-stochastic - separated to avoid generating random series when not needed
        if sigma == 0:
            return rho1 * x[t - 1] + rho2 * x[t - 2] + gamma + g

        # Stochastic
        else:
            epsilon = np.random.normal(0, 1, n)
            return rho1 * x[t - 1] + rho2 * x[t - 2] + gamma + g + sigma * epsilon[t]

    # Create list and set initial conditions
    y_t = [y_0, y_1]

    # Generate y_t series
    for t in range(2, n):

        # No government spending
        if g == 0:
            y_t.append(transition(y_t, t))

        # Government spending (no shock)
        elif g != 0 and duration == None:
            y_t.append(transition(y_t, t))

        # Permanent government spending shock
        elif duration == 'permanent':
            if t < g_t:
                y_t.append(transition(y_t, t, g=0))
            else:
                y_t.append(transition(y_t, t, g=g))

        # One-off government spending shock
        elif duration == 'one-off':
            if t == g_t:
                y_t.append(transition(y_t, t, g=g))
            else:
                y_t.append(transition(y_t, t, g=0))
    return y_t
```

A permanent government spending shock can be simulated as follows


```python
plot_y(y_stochastic_g(g=10, g_t=20, duration='permanent'))
```

```none
Roots are real and absolute values are less than zero; therfore get smooth convergence to a steady state
[ 0.7236068  0.2763932]
Roots are real
Roots are less than one
```


  
We can also see the response to a one time jump in government expenditures


```python
plot_y(y_stochastic_g(g=500, g_t=50, duration='one-off'))
```

```none
Roots are real and absolute values are less than zero; therfore get smooth convergence to a steady state
[ 0.7236068  0.2763932]
Roots are real
Roots are less than one
```


  

## Wrapping everything into a class

Up to now we have written functions to do the work

Now we’ll roll up our sleeves and write a Python class called Samuelson
for the Samuleson model


```python
class Samuelson():

    r"""This class represents the Samuelson model, otherwise known as the
    multiple-accelerator model. The model combines the Keynesian multipler
    with the accelerator theory of investment.

    The path of output is governed by a linear second-order difference equation

    .. math::

        Y_t =  + \alpha (1 + \beta) Y_{t-1} - \alpha \beta Y_{t-2}

    Parameters
    ----------
    y_0 : scalar
        Initial condition for Y_0
    y_1 : scalar
        Initial condition for Y_1
    alpha : scalar
        Marginal propensity to consume
    beta : scalar
        Accelerator coefficient
    n : int
        Number of iterations
    sigma : scalar
        Volatility parameter. Must be greater than or equal to 0. Set
        equal to 0 for non-stochastic model.
    g : scalar
        Government spending shock
    g_t : int
        Time at which government spending shock occurs. Must be specified
        when duration != None.
    duration : {None, 'permanent', 'one-off'}
        Specifies type of government spending shock. If none, government
        spending equal to g for all t.

    """

    def __init__(self,
                 y_0=100,
                 y_1=50,
                 alpha=1.3,
                 beta=0.2,
                 gamma=10,
                 n=100,
                 sigma=0,
                 g=0,
                 g_t=0,
                 duration=None):

        self.y_0, self.y_1, self.alpha, self.beta = y_0, y_1, alpha, beta
        self.n, self.g, self.g_t, self.duration = n, g, g_t, duration
        self.gamma, self.sigma = gamma, sigma
        self.rho1 = alpha + beta
        self.rho2 = -beta
        self.roots = np.roots([1, -self.rho1, -self.rho2])

    def root_type(self):
        if all(isinstance(root, complex) for root in self.roots):
            return 'Complex conjugate'
        elif len(self.roots) > 1:
            return 'Double real'
        else:
            return 'Single real'

    def root_less_than_one(self):
        if all(abs(root) < 1 for root in self.roots):
            return True

    def solution_type(self):
        rho1, rho2 = self.rho1, self.rho2
        discriminant = rho1 ** 2 + 4 * rho2
        if rho2 >= 1 + rho1 or rho2 <= -1:
            return 'Explosive oscillations'
        elif rho1 + rho2 >= 1:
            return 'Explosive growth'
        elif discriminant < 0:
            return 'Damped oscillations'
        else:
            return 'Steady state'

    def _transition(self, x, t, g):

        # Non-stochastic - separated to avoid generating random series when not needed
        if self.sigma == 0:
            return self.rho1 * x[t - 1] + self.rho2 * x[t - 2] + self.gamma + g

        # Stochastic
        else:
            epsilon = np.random.normal(0, 1, self.n)
            return self.rho1 * x[t - 1] + self.rho2 * x[t - 2] + self.gamma + g + self.sigma * epsilon[t]

    def generate_series(self):

        # Create list and set initial conditions
        y_t = [self.y_0, self.y_1]

        # Generate y_t series
        for t in range(2, self.n):

            # No government spending
            if self.g == 0:
                y_t.append(self._transition(y_t, t))

            # Government spending (no shock)
            elif self.g != 0 and self.duration == None:
                y_t.append(self._transition(y_t, t))

            # Permanent government spending shock
            elif self.duration == 'permanent':
                if t < self.g_t:
                    y_t.append(self._transition(y_t, t, g=0))
                else:
                    y_t.append(self._transition(y_t, t, g=self.g))

            # One-off government spending shock
            elif self.duration == 'one-off':
                if t == self.g_t:
                    y_t.append(self._transition(y_t, t, g=self.g))
                else:
                    y_t.append(self._transition(y_t, t, g=0))
        return y_t

    def summary(self):
        print('Summary\n' + '-' * 50)
        print('Root type: ' + self.root_type())
        print('Solution type: ' + self.solution_type())
        print('Roots: ' + str(self.roots))

        if self.root_less_than_one() == True:
            print('Absolute value of roots is less than one')
        else:
            print('Absolute value of roots is not less than one')

        if self.sigma > 0:
            print('Stochastic series with sigma = ' + str(self.sigma))
        else:
            print('Non-stochastic series')

        if self.g != 0:
            print('Government spending equal to ' + str(self.g))

        if self.duration != None:
            print(self.duration.capitalize() +
                  ' government spending shock at t = ' + str(self.g_t))

    def plot(self):
        fig, ax = plt.subplots(figsize=(12, 8))
        ax.plot(self.generate_series())
        ax.set(xlabel='Iteration', xlim=(0, self.n))
        ax.set_ylabel('$Y_t$', rotation=0)
        ax.grid()

        # Add parameter values to plot
        paramstr = '$\\alpha=%.2f$\n$\\beta=%.2f$\n$\\gamma=%.2f$\n$\\sigma=%.2f$\n$\\rho_1=%.2f$\n$\\rho_2=%.2f$'%(
                    self.alpha, self.beta, self.gamma, self.sigma, self.rho1, self.rho2)
        props = dict(fc='white', pad=10, alpha=0.5)
        ax.text(0.87, 0.05, paramstr, transform=ax.transAxes,
                fontsize=12, bbox=props, va='bottom')

        return fig

    def param_plot(self):

        # Uses the param_plot() function defined earlier (it is then able
        # to be used standalone or as part of the model)

        fig = param_plot()
        ax = fig.gca()

        # Add lambda values to legend
        for i, root in enumerate(self.roots):
            if isinstance(root, complex):
                operator = ['+', ''] # Need to fill operator for positive as string is split apart
                label = r'$\lambda_{0} = {1.real:.2f} {2} {1.imag:.2f}i$'.format(i+1, sam.roots[i], operator[i])
            else:
                label = r'$\lambda_{0} = {1.real:.2f}$'.format(i+1, sam.roots[i])
            ax.scatter(0, 0, 0, label=label) # dummy to add to legend

        # Add rho pair to plot
        ax.scatter(self.rho1, self.rho2, 100, 'red', '+', label=r'$(\ \rho_1, \ \rho_2 \ )$', zorder=5)

        plt.legend(fontsize=12, loc=3)

        return fig
```

### Illustration of Samuelson class

Now we’ll put our Samuelson class to work on an example


```python
sam = Samuelson(alpha=0.8, beta=0.5, sigma=2, g=10, g_t=20, duration='permanent')
sam.summary()
```

    Summary
    --------------------------------------------------
    Root type: Complex conjugate
    Solution type: Damped oscillations
    Roots: [ 0.65+0.27838822j  0.65-0.27838822j]
    Absolute value of roots is less than one
    Stochastic series with sigma = 2
    Government spending equal to 10
    Permanent government spending shock at t = 20


```none
Summary
---------------------------------------------------------
Root type: Complex conjugate
Solution type: Damped oscillations
Roots: [ 0.65+0.27838822j  0.65-0.27838822j]
Absolute value of roots is less than one
Stochastic series with sigma = 2
Government spending equal to 10
Permanent government spending shock at t = 20
```



```python
sam.plot()
plt.show()
```

  

### Using the graph

We’ll use our graph to show where the roots lie and how their location
is consistent with the behavior of the path just graphed

The red $+$ sign shows the location of the roots


```python
sam.param_plot()
plt.show()
```

  

## Using the LinearStateSpace class

It turns out that we can use the [QuantEcon.py](http://quantecon.org/python_index.html)
[LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class to do
much of the work that we have done from scratch above

Here is how we map the Samuelson model into an instance of a
LinearStateSpace class


```python
from quantecon import LinearStateSpace

""" This script maps the Samuelson model in the the LinearStateSpace class"""
alpha = 0.8
beta = 0.9
rho1 = alpha + beta
rho2 = -beta
gamma = 10
sigma = 1
g = 10
n = 100

A = [[1,        0,        0],
     [gamma + g, rho1, rho2],
     [0,        1,        0]]

G = [[gamma + g, rho1, rho2],         # this is Y_{t+1}
     [gamma,    alpha,    0],         # this is C_{t+1}
     [0,     beta,    -beta]]         # this is I_{t+1}

mu_0 = [1, 100, 100]
C = np.zeros((3,1))
C[1] = sigma # stochastic

sam_t = LinearStateSpace(A, C, G, mu_0=mu_0)

x, y = sam_t.simulate(ts_length=n)

fig, axes = plt.subplots(3, 1, sharex=True, figsize=(15, 8))
titles = ['Output ($Y_t$)', 'Consumption ($C_t$)', 'Investment ($I_t$)']
colors = ['darkblue', 'red', 'purple']
for ax, series, title, color in zip(axes, y, titles, colors):
    ax.plot(series, color=color)
    ax.set(title=title, xlim=(0, n))
    ax.grid()

axes[-1].set_xlabel('Iteration')

plt.show()
```

  

### Other methods in the LinearStateSpace class

Let’s plot **impulse response functions** for the instance of the
Samuelson model using a method in the LinearStateSpace class


```python
imres = sam_t.impulse_response()
imres = np.asarray(imres)
y1 = imres[:, :, 0]
y2 = imres[:, :, 1]
y1.shape
```

<table width=100%><tr style='background-color: #FFFFFF !important;'><td width=75%>$$
\left ( 2, \quad 6, \quad 1\right )
$$

</td><td width=25% style='text-align:center !important;'></td></tr></table>Now let’s compute the zeros of the characteristic polynomial by simply
calculating the eigenvalues of $ A $


```python
A = np.asarray(A)
w, v = np.linalg.eig(A)
print(w)
```

    [ 0.85+0.42130749j  0.85-0.42130749j  1.00+0.j        ]


```none
[ 0.85+0.42130749j  0.85-0.42130749j  1.00+0.j        ]
```


### Inheriting methods from LinearStateSpace

We could also create a subclass of LinearStateSpace (inheriting all its
methods and attributes) to add more functions to use


```python
class SamuelsonLSS(LinearStateSpace):

    """
    this subclass creates a Samuelson multiplier-accelerator model
    as a linear state space system
    """
    def __init__(self,
                 y_0=100,
                 y_1=100,
                 alpha=0.8,
                 beta=0.9,
                 gamma=10,
                 sigma=1,
                 g=10):

        self.alpha, self.beta = alpha, beta
        self.y_0, self.y_1, self.g = y_0, y_1, g
        self.gamma, self.sigma = gamma, sigma

        # Define intial conditions
        self.mu_0 = [1, y_0, y_1]

        self.rho1 = alpha + beta
        self.rho2 = -beta

        # Define transition matrix
        self.A = [[1,                 0,         0],
                  [gamma + g, self.rho1, self.rho2],
                  [0,                 1,         0]]

        # Define output matrix
        self.G = [[gamma + g, self.rho1, self.rho2],         # this is Y_{t+1}
                  [gamma,         alpha,         0],         # this is C_{t+1}
                  [0,              beta,     -beta]]         # this is I_{t+1}

        self.C = np.zeros((3, 1))
        self.C[1] = sigma  # stochastic

        # Initialize LSS with parameters from Samuleson model
        LinearStateSpace.__init__(self, self.A, self.C, self.G, mu_0=self.mu_0)

    def plot_simulation(self, ts_length=100, stationary=True):

        # Temporarily store original parameters
        temp_mu = self.mu_0
        temp_Sigma = self.Sigma_0

        # Set distribution parameters equal to their stationary values for simulation
        if stationary == True:
            try:
                self.mu_x, self.mu_y, self.sigma_x, self.sigma_y = self.stationary_distributions()
                self.mu_0 = self.mu_y
                self.Sigma_0 = self.sigma_y
            # Exception where no convergence achieved when calculating stationary distributions
            except ValueError:
                print('Stationary distribution does not exist')

        x, y = self.simulate(ts_length)

        fig, axes = plt.subplots(3, 1, sharex=True, figsize=(15, 8))
        titles = ['Output ($Y_t$)', 'Consumption ($C_t$)', 'Investment ($I_t$)']
        colors = ['darkblue', 'red', 'purple']
        for ax, series, title, color in zip(axes, y, titles, colors):
            ax.plot(series, color=color)
            ax.set(title=title, xlim=(0, n))
            ax.grid()

        axes[-1].set_xlabel('Iteration')

        # Reset distribution parameters to their initial values
        self.mu_0 = temp_mu
        self.Sigma_0 = temp_Sigma

        return fig

    def plot_irf(self, j=5):

        x, y = self.impulse_response(j)

        # Reshape into 3 x j matrix for plotting purposes
        yimf = np.array(y).flatten().reshape(j+1, 3).T

        fig, axes = plt.subplots(3, 1, sharex=True, figsize=(15, 8))
        labels = ['$Y_t$', '$C_t$', '$I_t$']
        colors = ['darkblue', 'red', 'purple']
        for ax, series, label, color in zip(axes, yimf, labels, colors):
            ax.plot(series, color=color)
            ax.set(xlim=(0, j))
            ax.set_ylabel(label, rotation=0, fontsize=14, labelpad=10)
            ax.grid()

        axes[0].set_title('Impulse Response Functions')
        axes[-1].set_xlabel('Iteration')

        return fig

    def multipliers(self, j=5):
        x, y = self.impulse_response(j)
        return np.sum(np.array(y).flatten().reshape(j+1, 3), axis=0)
```

### Illustrations

Let’s show how we can use the SamuelsonLSS


```python
samlss = SamuelsonLSS()
```


```python
samlss.plot_simulation(100, stationary=False)
plt.show()
```

  


```python
samlss.plot_simulation(100, stationary=True)
plt.show()
```

  


```python
samlss.plot_irf(100)
plt.show()
```

  


```python
samlss.multipliers()
```




    array([ 7.414389,  6.835896,  0.578493])



```none
array([ 7.414389,  6.835896,  0.578493])
```


## Pure multiplier model

Let’s shut down the accelerator by setting $ b=0 $ to get a pure
multiplier model

- the absence of cycles gives an idea about why Samuelson included the
  accelerator  


```python
pure_multiplier = SamuelsonLSS(alpha=0.95, beta=0)
```


```python
pure_multiplier.plot_simulation()
```

```none
Stationary distribution does not exist
```


  


```python
pure_multiplier = SamuelsonLSS(alpha=0.8, beta=0)
```


```python
pure_multiplier.plot_simulation()
```

  


```python
pure_multiplier.plot_irf(100)
```

  

## Summary

In this lecture, we wrote functions and classes to represent non-stochastic and
stochastic versions of the Samuelson (1939) multiplier-accelerator model, described
in [[Sam39]](zreferences.ipynb#samuelson1939)

We saw that different parameter values led to different output paths, which
could either be stationary, explosive, or ocillating

We also were able to represent the model using the [QuantEcon.py](http://quantecon.org/python_index.html)
[LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class
