Analysing the properties of ordinary least squares estimators of regression models in the presence of time series variables

Abstract

Regression analysis is amongst one of the most popular statistical techniques which has been studied extensively in the past decades. A different approach to the classical linear regression arises when the dependent variable and its predictors are regarded as time series variables, therefore the observations in the study are no longer independent. This dissertation studies the properties of the ordinary least squares estimators when time series variables are considered and when the assumptions of classical linear regression are violated. The distribution of the estimator when these assumptions are not satisfied is derived and the relevant time series regression models are applied to various datasets to model the accounting revenue and turnover of a local betting company