Essentials of Time Series for Financial Applications

Chapter 1

Linear Regression Model

Abstract

This chapter introduces the linear regression model used in applied time series analysis to investigate relations among variables. In Section 1.1, the basic tools and assumptions underlying the model are presented; then the chapter shows how to derive point estimates of the parameters using three possible estimation methods, that is, ordinary least square, generalized least squares, and maximum likelihood; moreover, a range of methods to build tests of hypotheses on the parameters are developed. In Section 1.2, the chapter explains how to deal with violations of the hypotheses of the linear regression framework. Section 1.3 deals with the selection of the appropriate regressors and presents some of the ...