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SAS for Forecasting Time Series, Third Edition, 3rd Edition
book

SAS for Forecasting Time Series, Third Edition, 3rd Edition

by John C. Brocklebank, David A. Dickey, Bong Choi
March 2018
Beginner to intermediate content levelBeginner to intermediate
384 pages
12h 2m
English
SAS Institute
Content preview from SAS for Forecasting Time Series, Third Edition, 3rd Edition

Chapter 2: Simple Models: Autoregression

2.1 Introduction

2.1.1 Terminology and Notation

2.1.2 Statistical Background

2.2 Forecasting

2.2.1 PROC ARIMA for Forecasting

2.2.2 Backshift Notation B for Time Series

2.2.3 Yule-Walker Equations for Covariances

2.3 Fitting an AR Model in PROC REG

2.1 Introduction

A simple and yet quite useful model, the order 1 autoregressive, AR(1), model is used in this chapter to introduce some of the basic ideas in time series analysis and forecasting.

2.1.1 Terminology and Notation

Often, you can forecast series Yt simply based on past values Yt–1, Yt–2, …. For example, suppose Yt satisfies the following:

Ytμ=ρ(Yt-1μ)+et   (2.1)

where et is a sequence of uncorrelated N(0, σ2) variables. The term for such an

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Publisher Resources

ISBN: 9781629605449