# ch04 - Chapter 4 Estimation in the Time Domain Li Chen...

This preview shows pages 1–5. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Chapter 4: Estimation in the Time Domain Li Chen Department of Mathematics University of Bristol 1 / 10 Outline Fitting an ARIMA process to an observed time series proceeds in three stages: 1. identification of the order ( p , d , q ), 2. estimation of the model parameters, 3. diagnostic checking of the fitted model. In this chapter we will learn: I modeling an autoregressive process (AR) I modeling a moving average process (MA) I modeling an ARIMA process 2 / 10 4.1 Modeling AR( p ) Given x 1 , . . . , x N suppose we identify the process as an AR( p ). There are two steps: (a) identifying p , (b) estimating parameters { α 1 , . . . , α p } . We will consider (b) first. Suppose we know p . Then we will use the model X t- μ = α 1 ( X t- 1- μ ) + · · · + α p ( X t- p- μ ) + Z t , t = p + 1 , . . . , N to model a time series { X t } with mean μ . 3 / 10 4.1.1 Estimating the parameters of an AR process Least squares estimation Given N observations x 1 , . . . , x N the parameters...
View Full Document

## This note was uploaded on 10/19/2009 for the course MATH 611 taught by Professor Jsdkasj during the Spring '09 term at Kansas.

### Page1 / 10

ch04 - Chapter 4 Estimation in the Time Domain Li Chen...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online