A p-order autoregressive process, denoted AR(p), takes the form
Thinking of the subscripts i as representing time, we see that the value of y at time i is a linear function of y at earlier times plus a fixed constant and a random error term. Similar to the ordinary linear regression model, we assume that the error terms are independently distributed based on a normal distribution with zero mean and a constant variance σ2 and that the error terms are independent of the y values.
Topics:
- Basic Concepts
- Characteristic Equation
- Partial Autocorrelation
- Finding Model Coefficients using ACF/PACF
- Finding Model Coefficients using Linear Regression
- Lag Function Representation
- Augmented Dickey-Fuller Test
- Other Unit Root Tests
