This thoroughly revised second edition of an upper-level undergraduate/graduate text describes many major techniques of forecasting used in economics and business. This is the only time series book to concentrate on the forecasting of economic data and to cover such a broad range of topics. The key features are: explains how to specify and evaluate simple models from the time series and econometric approaches; places special emphasis on the information that is derived from the evaluation and combinations of forecasts; discusses the topics of technological and population forecasting; includes an expanded chapter on regression techniques; presents a practical forecasting project which runs throughout the text; includes an appendix on basic statistical concepts.

Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing.
*Addresses economic forecasting methodology, forecasting models, forecasting with different data structures, and the applications of forecasting methods
*Insights within this volume can be applied to economics, finance and marketing disciplines

This book helps economists with the difficult task of constructing econometric models and will be especially useful to those taking courses in applied econometrics who need to learn how to evaluate the validity of the theories and techniques they are taught. The volume contains seventeen papers by the leading authorities in the field, divided into four groups, to each of which the editor provides an introduction. The whole volume is prefaced with an editorial discussion of the controversies of the subject. The methods critically discussed include the traditional ones, such as vector auto-regressions; Bayesian techniques; and the comprehensive modelling strategy advocated by reseachers at the London School of Economics. The papers vary in the degree of sophistication used, but anyone reading the book should gain a sound knowledge of the practical difficulties involved in model specification, evaluation, and interpretation.

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 leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

This thoroughly revised second edition of an upper-level undergraduate/graduate text describes many major techniques of forecasting used in economics and business. This is the only time series book to concentrate on the forecasting of economic data and to cover such a broad range of topics. Its key features are: gives a complete description, with applications, of the Box-Jenkins single series modeling techniques; extends the Box-Jenkins techniques to multivariate cases; compares forecasts from purely statistical and econometric models; pays careful attention to such problems as how to evaluate and compare forecasts; covers nonstationary and nonlinear models, co-integration and error-correction models.