This is a survey of recent developments in the field of cointegration, which links long run components of a pair or of a group of series. The authors present ideas in a non-technical way which will enable economists with training in econometrics to understand and appreciate current research.

Major developments in the analysis of non-stationary time series and co-integration are shown in this book. Papers include David Hendry's work on forecasting, Peter Phillip's work on Bayesian models, Svend Hylleberg's work on seasonality, and Adrian Pagan's work on real business cycle models....

Providing an introduction to VAR modelling and how it can be applied, this book focuses on the properties of the cointegrated VAR model and its implications for macroeconomic inference when data are non-stationary. It gives insights into the links between statistical econometric modelling and...

This volume presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. It is intended to give a self-contained presentation of the methods and applicative issues.

Long memory time series are characterized by a strong dependence between distant events. Various methods and their theoretical properties are discussed along with their empirical applications in this student textbook.

Stochastic volatility is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. This work brings together some of the main papers that have influenced this field, and shows that the development of this...

Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book provides an introduction to the method combined with a unified treatment of GMM statistical theory and a survey of the important developments in the field.

This text provides a self-contained account of periodic models for seasonally observed economic time series with stochastic trends. The analysis considers econometric theory, Monte Carlo simulation and forecasting, and it is illuminated with empirical time series.

In the early 1980s, R.F. Engle pioneered the econometric technique of auto-regressive conditional heteroskedasticity (ARCH). This collection of essays explores both applied and theoretical ARCH models. Its introduction traces the development of this field of econometrics.