Diagnostic Checks in Time Series: 1st Edition (Hardback) book cover

Diagnostic Checks in Time Series

1st Edition

By Wai Keung Li

Chapman and Hall/CRC

216 pages | 22 B/W Illus.

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Hardback: 9781584883371
pub: 2003-12-29
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Description

Diagnostic checking is an important step in the modeling process. But while the literature on diagnostic checks is quite extensive and many texts on time series modeling are available, it still remains difficult to find a book that adequately covers methods for performing diagnostic checks.

Diagnostic Checks in Time Series helps to fill that gap. Author Wai Keung Li--one of the world's top authorities in time series modeling--concentrates on diagnostic checks for stationary time series and covers a range of different linear and nonlinear models, from various ARMA, threshold type, and bilinear models to conditional non-Gaussian and autoregressive heteroscedasticity (ARCH) models. Because of its broad applicability, the portmanteau goodness-of-fit test receives particular attention, as does the score test. Unlike most treatments, the author's approach is a practical one, and he looks at each topic through the eyes of a model builder rather than a mathematical statistician.

This book brings together the widely scattered literature on the subject, and with clear explanations and focus on applications, it guides readers through the final stages of their modeling efforts. With Diagnostic Checks in Time Series, you will understand the relative merits of the models discussed, know how to estimate these models, and often find ways to improve a model.

Reviews

"There are many books on time series analysis but this is the first monograph specialized to diagnostic checking. … The author is a known specialist in time series modelling. His approach is a practical one and each topic is presented from a model builder's point of view. … [V]ery useful for statisticians working in time series analysis."

- EMS Newsletter

"[T]he author has adopted an easy-to-follow style which takes the reader to the frontier of the literature painlessly."

- Journal of the Royal Statistical Society

"There have been several excellent monographs on the diagnostics of linear models, but this is the first and possibly definitive one for stationary time series modeling. It is of great value in bringing together the diverse literature on the topic, over three hundred references are given, and integrating them into a coherent whole…Whatever type of time series model you are fitting, linear or nonlinear, volatile or not, turn to this monograph for help in testing its goodness-of-fit."

- ISI Short Book Reviews

Table of Contents

INTRODUCTION

DIAGNOSTIC CHECKS FOR UNIVARIATE LINEAR MODELS

Introduction

The Asymptotic Distribution of the Residual Autocorrelation Distribution

Modifications of the Portmanteau Statistic

Extension to Multiplicative Seasonal ARMA Models

Relation with the Lagrange Multiplier Test

A Test Based on the Residual Partial Autocorrelation test

A Test Based on the Residual Correlation Matrix test

Extension to Periodic Autoregressions

THE MULTIVARIATE LINEAR CASE

The Vector ARMA model

Granger Causality Tests

Transfer Function Noise (TFN) Modeling

ROBUST MODELING AND ROBUST DIAGNOSTIC CHECKING

A Robust Portmanteau Test

A Robust Residual Cross-Correlation Test

A Robust Estimation Method for Vector Time Series

The Trimmed Portmanteau Statistic

NONLINEAR MODELS

Introduction

Tests for General Nonlinear Structure

Tests for Linear vs. Specific Nonlinear Models

Goodness-of-Fit Tests for Nonlinear Time Series

Choosing Two Different Families of Nonlinear Models

CONDITIONAL HETEROSCEDASTICITY MODELS

The Autoregressive Conditional Heteroscedastic Model

Checks for the Presence of ARCH

Diagnostic Checking for ARCH Models

Diagnostics for Multivariate ARCH models

Testing for Causality in the Variance

FRACTIONALLY DIFFERENCED PROCESS

Introduction

Methods of Estimation

A Model Diagnostic Statistic

Diagnostics for Fractional Differencing

MISCELLANEOUS MODELS AND TOPICS

ARMA Models with Non-Gaussian Errors

Other Non-Gaussian time Series

The Autoregressive Conditional Duration Model

A Power Transformation to Induce Normality

Epilogue

About the Series

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General