# Time in years; one update per month
start_time <- 0
end_time <- 5
delta_t <- 1 / 12
time <- seq(start_time, end_time, by = delta_t)
month <- 0:(length(time) - 1)
# Parameters and initial conditions
annual_inflation_rate <- 0.05
annual_interest_rate <- 0.04
annual_depreciation_rate <- 0.08
monthly_contribution <- 600
new_car_initial <- 30000
trade_in_initial <- 13000
savings_initial <- 6000
# Create the three stock vectors.
# TODO
# Store the three initial conditions.
# TODO
# Update all three stocks one month at a time.
for (i in 2:length(time)) {
# TODO
}
# Combine the results and calculate the cash needed.
# TODOThe question
In one or two sentences, explain the decision this model will help make.
Model and assumptions
Briefly describe how each quantity changes during one month:
- new-car price;
- current-car trade-in value; and
- savings balance.
State two assumptions made by this model.
Base-case simulation
Verification
Calculate the new-car price, trade-in value, and savings after the first month by hand. Show your calculation, then display the first two rows of your simulation and explain whether they agree.
# Display the first two rows here.
# TODOResult
Find the first month when savings are at least as large as the cash needed.
# Use the first affordable position to display the crossing month and the
# preceding month. Remember that a vector position is not a month number.
# affordable_positions <- which(results$savings >= results$cash_needed)
# TODOIn a few sentences, report the first affordable month and explain how you confirmed it was the first.
Graph
Create one graph showing savings and cash needed over time.
# Your graph goes here. Include a title, labeled axes, two distinguishable
# lines, and a legend.
# TODOWhat does the graph show? Describe where the two lines cross.
One experiment
Change the monthly contribution from $600 to either $400 or $800.
Before running the modified model, predict whether the buyer will afford the car earlier or later and explain why.
Prediction: Replace this sentence with your prediction.
# Duplicate or adapt your simulation for the new monthly contribution.
# A function is not required.
# TODOReport the new purchase month. Was your prediction correct? Explain the difference from the base case.
Conclusion
Write a short recommendation to the buyer. Include:
- the base-case purchase month;
- what the experiment revealed; and
- two important limitations of the model.