Introduction

Any ordered temporal variable may be regarded as a time series. These variables are used to understand past behaviour and to predict the future. In this way, they are able to inform the decision-making process, which is important for many aspects of life. However, the obvious correlation introduced by the sampling of adjacent points in time can severely restrict the applicability of the many conventional statistical methods that usually depend on the assumption that these adjacent observations are independent and identically distributed. This has led to the development of a number of innovative solutions that facilitate investigations into the study of time series analysis.1 During this semester, we will focus on the modelling of economic and financial time series. Many of these variables contain trends and seasonal variations that may be modelled as deterministic functions of time or as stochastic processes.2 The methods that have been used to analyse these variables incorporate two s

Any ordered temporal variable may be regarded as a time series. These variables are used to understand past behaviour and to predict the future. In this way, they are able to inform the decision-making process, which is important for many aspects of life. However, the obvious correlation introduced by the sampling of adjacent points in time can severely restrict the applicability of the many conventional statistical methods that usually depend on the assumption that these adjacent observations are independent and identically distributed. This has led to the development of a number of innovativ