Estimate the autocorrelation function (ACF) for a moving average
Now that you've simulated some MA data using the arima.sim() command, you may want to estimate the autocorrelation functions (ACF) for your data. As in the previous chapter, you can use the acf() command to generate plots of the autocorrelation in your MA data.
In this exercise, you'll use acf() to estimate the ACF for three simulated MA series, x, y, and z. These series have slope parameters of 0.4, 0.9, and -0.75, respectively, and are shown in the figure on the right.
This exercise is part of the course
Time Series Analysis in R
Exercise instructions
- Use three calls to
acf()to estimate the autocorrelation functions forx,y, andz, respectively.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate ACF for x
acf(___)
# Calculate ACF for y
# Calculate ACF for z