# Lesson 14: Time Series & Autocorrelation

Recall that one of the assumptions when building a linear regression model is that the errors are independent. This section discusses methods for dealing with dependent errors. In particular, the dependency usually appears because of a temporal component. Error terms correlated over time are said to be **autocorrelated** or **serially correlated**. When error terms are autocorrelated, some issues arise when using ordinary least squares. These problems are:

- Estimated regression coefficients are still unbiased, but they no longer have the minimum variance property.
- The MSE may seriously underestimate the true variance of the errors.
- The standard error of the regression coefficients may seriously underestimate the true standard deviation of the estimated regression coefficients.
- Statistical intervals and inference procedures are no longer strictly applicable.

We also consider the setting where a data set has a temporal component that affects the analysis.