Written by one of the world's foremost authorities in time series modelling, this book explores goodness of fit tests in time series analysis. Starting with linear models, the author proceeds to nonlinear modelling with extensions to long-memory and generalized linear models--all areas of interest and activity. The focus is firmly on practical matters, and the author presents a range of applications, particularly from the financial arena. Until now, published work in this area has been scattered throughout the literature. Researchers and practitioners alike will welcome this book as a reference that will guide them through the final stages of their modelling tasks.

This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.

This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.