Introduction

Your average economists.

Background

# Download data
for i in $(seq 1990 2014)
        do wget http://www.energy.gov.on.ca/fuelupload/ONTREG$i.csv
done

# Retain the header
head -n 2 ONTREG1990.csv | sed 1d > ONTREG_merged.csv

# Loop over the files and use sed to extract the relevant lines
for i in $(seq 1990 2014)
        do
        tail -n 15 ONTREG$i.csv | sed 13,15d | sed 's/./-01-'$i',/4' >> ONTREG_merged.csv
        done

Wow, that really is a cliff, isn’t it? The average Ontario Gas Price hasn’t been as low as it was in Dec 2014 since the fall of 2010 (Sep 2010, $1.01) and the WTI not since about May of that year.

Now to the fun stuff. Let’s read the data into R and do some hacky time series analysis.

library(ts)
library(forecast)

# Read in the Ontario Gas Price Data
data <- read.csv(file="ONTREG_merged.csv", header=T, sep=",")

# Read in the WTI oil price data
WTI_data <- read.csv(file='DCOILWTICO.csv',header=F, col.names=c("Date", "Value"))

# Create a time series object for the WTI and Ontario Avg
WTI <- ts(data=WTI_data$Value, frequency=12, start=c(1990,1), end=c(2014,12))
ON <- ts(data=data$ON.Avg, frequency=12, start=c(1990,1), end=c(2014,12))

# Plot and compare
combined <- cbind(WTI, ON)
plot(combined)

We get a comparison plot of the data:
And we can look at the Ontario Gas Price as a function of the WTI. Linear is on the left, log-log on the right.

Next we build lm model objects and look at the diagnostics. I'll spare the details, but I feel better about the log-log, so we'll go with that.

# Create linear models (normal and log-log)
l1 <- lm(ON ~ WTI, data=combined)
l2 <- lm(log(ON) ~ log(WTI), data=combined)

# Compare relative performance
summary(l1)
summary(l2)
plot(l1)
plot(l2)

# Plot
plot(ON ~ WTI, data=combined, pch=16, cex=0.3)
abline(l1)
plot(log(ON) ~ log(WTI), data=combined, pch=16, cex=0.3)
abline(l2)

# Read in WTI forecast data
WTI_forecast <- read.csv(file="WTI_forecast.csv", header=F, sep=",", col.names=c("Date", "Value"))

# Forecast Ontario Gas Price
fit <- forecast(l2, newdata=data.frame(WTI=WTI_forecast$Value))

# Unlog
fit$mean <- exp(fit$mean)
fit$lower <- exp(fit$lower)
fit$upper <- exp(fit$upper)
fit$x <- exp(fit$x)

# Plot
plot(fit, ylab='Ontario Average Gas Price (cents/L)')

And there you have it! Putting the forecast data (blue line) and the WTI forecast back into our original graph, we can compare the two:


It's that easy, right? That's all there is to it?

Conclusion

Fuel Price Data: Regular Unleaded Gasoline 2014 (Ontario Ministry of Energy):
Short-Term Energy Outlook (U.S. Energy Information Administration)