# quant-econ Solutions: Python by Example

## Exercise 1



In [28]:

def factorial(n):
k = 1
for i in range(n):
k = k * (i + 1)
return k

factorial(4)




Out[28]:

24



## Exercise 2



In [29]:

from random import uniform

def binomial_rv(n, p):
count = 0
for i in range(n):
U = uniform(0, 1)
if U < p:
count = count + 1    # Or count += 1
return count

binomial_rv(10, 0.5)




Out[29]:

2



## Exercise 3

Consider the circle of diameter 1 embedded in the unit square

Let $A$ be its area and let $r=1/2$ be its radius

If we know $\pi$ then we can compute $A$ via $A = \pi r^2$

But here the point is to compute $\pi$, which we can do by $\pi = A / r^2$

Summary: If we can estimate the area of the unit circle, then dividing by $r^2 = (1/2)^2 = 1/4$ gives an estimate of $\pi$

We estimate the area by sampling bivariate uniforms and looking at the fraction that fall into the unit circle



In [30]:

from __future__ import division  # Omit if using Python 3.x
from math import sqrt

n = 100000

count = 0
for i in range(n):
u, v = uniform(0, 1), uniform(0, 1)
d = sqrt((u - 0.5)**2 + (v - 0.5)**2)
if d < 0.5:
count += 1

area_estimate = count / n

print area_estimate * 4  # dividing by radius**2




3.1418



## Exercise 4



In [31]:

payoff = 0
count = 0

for i in range(10):
U = uniform(0, 1)
count = count + 1 if U < 0.5 else 0
if count == 3:
payoff = 1

print payoff




1



## Exercise 5

The next line embeds all subsequent figures in the browser itself



In [35]:

%matplotlib inline




In [36]:

from pylab import plot, show, legend
from random import normalvariate

alpha = 0.9
ts_length = 200
current_x = 0

x_values = []
for i in range(ts_length):
x_values.append(current_x)
current_x = alpha * current_x + normalvariate(0, 1)
plot(x_values, 'b-')




Out[36]:

[<matplotlib.lines.Line2D at 0x3ae7d50>]



## Exercise 6



In [37]:

alphas = [0.0, 0.8, 0.98]
ts_length = 200

for alpha in alphas:
x_values = []
current_x = 0
for i in range(ts_length):
x_values.append(current_x)
current_x = alpha * current_x + normalvariate(0, 1)
plot(x_values, label='alpha = ' + str(alpha))
legend()




Out[37]:

<matplotlib.legend.Legend at 0x378e6d0>




In [ ]: