Univariate time series modelling and forecasting

Abstract

Learning Outcomes In this chapter, you will learn how to Explain the defining characteristics of various types of stochastic processes Identify the appropriate time series model for a given data series Produce forecasts for ARMA and exponential smoothing models Evaluate the accuracy of predictions using various metrics Estimate time series models and produce forecasts from them in EViews Introduction Univariate time series models are a class of specifications where one attempts to model and to predict financial variables using only information contained in their own past values and possibly current and past values of an error term. This practice can be contrasted with structural models , which are multivariate in nature, and attempt to explain changes in a variable by reference to the movements in the current or past values of other (explanatory) variables. Time series models are usually a-theoretical, implying that their construction and use is not based upon any underlying theoretical model of the behaviour of a variable. Instead, time series models are an attempt to capture empirically relevant features of the observed data that may have arisen from a variety of different (but unspecified) structural models. An important class of time series models is the family of AutoRegressive Integrated Moving Average (ARIMA) models, usually associated with Box and Jenkins (1976). Time series models may be useful when a structural model is inappropriate. For example, suppose that there is some variable y t whose movements a researcher wishes to explain.