绝对的好书！
2004 by Chapman & Hall/CRC

This book is about diagnostic checking for time series models over discrete
time. There are many texts and monographs on time series modeling
but almost none of them has diagnostic checking as the major
focus.

196 Pages   Format: PDF
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Contents
Preface
1 Introduction
2 Diagnostic checks for univariate linear models
2.1 Introduction
2.2 The asymptotic distribution of the residual autocorrelation
distribution
2.3 Modifications of the portmanteau statistic
2.4 Extension to multiplicative seasonal ARMA models
2.5 Relation with the Lagrange multiplier test
2.6 A test based on the residual partial autocorrelation test
2.7 A test based on the residual correlation matrix test
2.8 Extension to periodic autoregressions
3 The multivariate linear case
3.1 The vector ARMA model
3.2 Granger causality tests
3.3 Transfer function noise (TFN) modeling
4 Robust modeling and diagnostic checking
4.1 A robust portmanteau test
4.2 A robust residual cross-correlation test
4.3 A robust estimation method for vector time series
4.4 The trimmed portmanteau statistic
5 Nonlinear models
5.1 Introduction
5.2 Tests for general nonlinear structure
5.3 Tests for linear vs. specific nonlinear models
5.4 Goodness-of-fit tests for nonlinear time series
5.5 Choosing two different families of nonlinear models
6 Conditional heteroscedasticity models
6.1 The autoregressive conditional heteroscedastic model
6.2 Checks for the presence of ARCH
6.3 Diagnostic checking for ARCH models
6.4 Diagnostics for multivariate ARCH models
6.5 Testing for causality in the variance
……………………