Coefficient Interpretation

The coefficients in an autoregressive model represent the impact of the lagged observations on the current observation. They provide insights into the patterns and dynamics of the time series data.

Residual Analysis

Residual analysis is done to assess the model’s goodness of fit. It involves studying the residuals to check for any remaining patterns and ensure that the model captures the underlying structure of the data.

Popular Types of Autoregressive Models

AR(1) Model

The AR(1) model is the simplest autoregressive model, where the current observation is linearly dependent on the previous observation. It is characterized by one lagged variable and is widely used in forecasting applications.