Time Series Modeling

Abstract

In this chapter, the reader is introduced to time series modeling. The science (and art) of time series modeling reflects the times series models of Professors Box and Jenkins, Granger, and Hendry. Many economic time series follow near-random walks or random walk with drift processes. This chapter uses the time series modeling of real Gross Domestic product, GDP, as a time series of interest. Time series models can be univariate, where a time series is modeled only by its past values, or multivariate, in which an input series leads an output series, such as a composite index of leading economic indicators, LEI, that can be used as an input to a transfer function model of real Gross Domestic Product, GDP. Economic indicators are descriptive and anticipatory time-series data can be used to analyze and forecast changing business conditions. Cyclical indicators are comprehensive series that are systematically related to the business cycle. Business cycles are recurrent sequences of expansions and contractions in aggregate economic activity. Coincident indicators have cyclical movements that approximately correspond with the overall business cycle expansions and contractions. Leading indicators reach their turning points before the corresponding business cycle turns. The lagging indicators reach their turning points after the corresponding turns in the business cycle.