Time Series: The Time Domain

Abstract

Many of the data sets which might best be handled by one of the approaches of the previous chapters would often be treated by a classical time series analysis, assuming a normal distribution. Most often, this simply amounts to fitting a multiple regression, with (a dynamic model) or without (a static model) lagged variables. Checks are then made on the various assumptions underlying the model: serial correlation of the residuals, non-normality, heteroscedasticity, and mis-specification of the linear model. Except for the first, these are the common verifications that a GLIM user applies to any model.