Stochastic nature of Monte Carlo simulation
In the previous exercise, you modeled information deterministically. You'll now attempt to estimate future inflation with a stochastic model, using a Monte Carlo simulation.
Recall that stochastic models simulate randomness in variables by using sampling. This randomness means that each simulation will likely arrive at a different expected outcome, even if the inputs are the same. We saw this in the video by running Monte Carlo simulations with different seeds.
In this exercise, assume 8.6% inflation in 2022 and a stochastic increase of 1%, 2%, or 3% each year over the previous year (with equal probabilities of 1%, 2%, or 3%) for the following years. What will the inflation rate look like in 2050 under these assumptions?
The random package has already been imported for you as random.
This exercise is part of the course
Monte Carlo Simulations in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Complete the function definition by defining the yearly_increase variable
def monte_carlo_inflation(year, seed):
random.seed(seed)
inflation_rate = 8.6
yearly_increase = ____
for i in range(year - 2022):
inflation_rate = inflation_rate*((100 + yearly_increase)/100)
return(inflation_rate)