David F. Hendry is a seminal figure in modern econometrics. He has pioneered the LSE approach to econometrics, and his influence is wide ranging. This book is a collection of papers dedicated to him and his work. Many internationally renowned econometricians who have collaborated with Hendry or have been influenced by his research have contributed to this volume, which provides a reflection on the recent advances in econometrics and considers the future progress for the methodology of econometrics. Central themes of the book include dynamic modelling and the properties of time series data, model selection and model evaluation, forecasting, policy analysis, exogeneity and causality, and encompassing. The book strikes a balance between econometric theory and empirical work, and demonstrates the influence that Hendry's research has had on the direction of modern econometrics. Contributors include: Karim Abadir, Anindya Banerjee, Gunnar Bardsen, Andreas Beyer, Mike Clements, James Davidson, Juan Dolado, Jurgen Doornik, Robert Engle, Neil Ericsson, Jesus Gonzalo, Clive Granger, David Hendry, Kevin Hoover, Soren Johansen, Katarina Juselius, Steven Kamin, Pauline Kennedy, Maozu Lu, Massimiliano Marcellino, Laura Mayoral, Grayham Mizon, Bent Nielsen, Ragnor Nymoen, Jim Stock, Pravin Trivedi, Paolo Paruolo, Mark Watson, Hal White, and David Zimmer.

This volume presents original and up-to-date studies in unobserved components (UC) time series models from both theoretical and methodological perspectives. It also presents empirical studies where the UC time series methodology is adopted. Drawing on the intellectual influence of Andrew Harvey, the work covers three main topics: the theory and methodology for unobserved components time series models; applications of unobserved components time series models; and time series econometrics and estimation and testing. These types of time series models have seen wide application in economics, statistics, finance, climate change, engineering, biostatistics, and sports statistics. The volume effectively provides a key review into relevant research directions for UC time series econometrics and will be of interest to econometricians, time series statisticians, and practitioners (government, central banks, business) in time series analysis and forecasting, as well to researchers and graduate students in statistics, econometrics, and engineering.

David F. Hendry is a seminal figure in modern econometrics. He has pioneered the LSE approach to econometrics, and his influence is wide ranging. This book is a collection of papers dedicated to him and his work. Many internationally renowned econometricians who have collaborated with Hendry or have been influenced by his research have contributed to this volume, which provides a reflection on the recent advances in econometrics and considers the future progress for the methodology of econometrics. Central themes of the book include dynamic modelling and the properties of time series data, model selection and model evaluation, forecasting, policy analysis, exogeneity and causality, and encompassing. The book strikes a balance between econometric theory and empirical work, and demonstrates the influence that Hendry's research has had on the direction of modern econometrics.
Contributors include: Karim Abadir, Anindya Banerjee, Gunnar Bardsen, Andreas Beyer, Mike Clements, James Davidson, Juan Dolado, Jurgen Doornik, Robert Engle, Neil Ericsson, Jesus Gonzalo, Clive Granger, David Hendry, Kevin Hoover, Soren Johansen, Katarina Juselius, Steven Kamin, Pauline Kennedy, Maozu Lu, Massimiliano Marcellino, Laura Mayoral, Grayham Mizon, Bent Nielsen, Ragnor Nymoen, Jim Stock, Pravin Trivedi, Paolo Paruolo, Mark Watson, Hal White, and David Zimmer."

Neil Shephard has brought together a set of classic and central papers that have contributed to our understanding of financial volatility. They cover stocks, bonds and currencies and range from 1973 up to 2001. Shephard, a leading researcher in the field, provides a substantial introduction in which he discusses all major issues involved.

This 2004 volume offers a broad overview of developments in the theory and applications of state space modeling. With fourteen chapters from twenty-three contributors, it offers a unique synthesis of state space methods and unobserved component models that are important in a wide range of subjects, including economics, finance, environmental science, medicine and engineering. The book is divided into four sections: introductory papers, testing, Bayesian inference and the bootstrap, and applications. It will give those unfamiliar with state space models a flavour of the work being carried out as well as providing experts with valuable state of the art summaries of different topics. Offering a useful reference for all, this accessible volume makes a significant contribution to the literature of this discipline.