In this interesting survey of recent developments in the field of cointegration, the authors discuss how cointegration (the linking of long run components of a pair or of a group or series), can be used to discuss some types of equilibrium and to introduce those equilibria into time-series models in a fairly uncontroversial way. The authors discuss the basic ideas in their introduction and the final chapters review the most recent developments in the field in a non-technical manner.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which...

This book offers an up-to-date coverage of the basic principles and tools of Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations, and the long available analytical results of Bayesian inference for linear regression models.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic...

This volume in the series Advanced Texts in Econometrics explains recent theoretical developments in the econometric modelling of relationships between different statistical series. Clive Granger and Timo Terasvirta illustrate ways of using dynamic, multivariate analysis techniques to provide models of nonlinear relationships between variables. They pay particular attention to the case of a single dependent variable modelled by a few explanatory variables and the lagged dependent variable in nonlinear form. They also discuss the division of nonlinear relationships into parametric and...

Although there has been rapid development in the field of unit roots and cointegration, this progress has taken divergent directions, and has been subjected to criticism. This monograph clearly relates cointegration to economic theories and describes cointegrated regression as a revolution in econometric methods for macroeconomics. It provides a guide for the selection of appropriate inference methods to study macroeconomic relations. The discussion of unit roots and cointegration starts from first principles, builds up explanations of concepts and techniques step-by-step, and ultimately...

Major developments in the analysis of non-stationary time series and cointegration are described in this study. 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. Other topics covered include an overview of the different estimators of cointegrating relationships, and a new test of cointegration. Applications find roots in macroeconomic series, test the Fisher Hypothesis, test money demand functions, and test for inflation bubbles.

In the early 1980s, R. F. Engle pioneered the econometric technique of Auto-Regressive Conditional Heteroskedasticity (ARCH), which has subsequently generated a very considerable literature. This collection brings together readings on ARCH models, both applied and theoretical, half by Engle himself and half by other econometricians working in the field. It begins with an introduction by the editor which traces the development of the field.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent...

This book provides a self-contained account of periodic models for seasonally observed economic time series with stochastic trends. Two key concepts are periodic integration and periodic cointegration. Periodic integration implies that a seasonally varying differencing filter is required to remove a stochastic trend. Periodic cointegration amounts to allowing cointegration paort-term adjustment parameters to vary with the season. The emphasis is on useful econrameters and shometric models that explicitly describe seasonal variation and can reasonably be interpreted in terms of economic...

This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results. The first part of the book deals with model selection, diagnostic checking and forecasting of univariate periodic autoregressive models. Tests for periodic integration, are discussed, and an extensive discussion of the role of deterministic regressors in testing for...

Panel data econometrics uses both time series and cross-sectional data sets that have repeated observations over time for the same individuals (individuals can be workers, households, firms, industries, regions, or countries). This book reviews the most important topics in the subject. The three parts, dealing with static models, dynamic models, and discrete choice and related models are organized around the themes of controlling for unobserved heterogeneity and modelling dynamic responses and error components.
About the Series
Advanced Texts in Econometrics is a distinguished and...

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.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and...