Time Series Regression Models
Multiple linear regression models assume that a response variable is a linear combination of predictor variables, a constant, and a random disturbance. If the variables are time series processes, then classical linear model assumptions, such as spherical disturbances, might not hold. For more details on time series regression models and their departures from classical linear model assumptions, see Time Series Regression I: Linear Models.
- Autocorrelated and Heteroscedastic Disturbances
Regression models with nonspherical errors, and HAC and FGLS estimators
- Bayesian Linear Regression Models
Posterior estimation, simulation, and predictor variable selection using a variety of prior models for the regression coefficients and disturbance variance