Modelling trends and cycles in economic time series has a long history in empirical economics, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. In particular there was a shift towards using stochastic, rather than deterministic trend formulations. This research was given further impetus by the rise of real business cycle theories, which offered an alternative approach to detrending via more sophisticated use of moving averages, and to the concept of cointegration, which can be interpreted as implying the presence of common trends in a group of related time series. Terence Mills introduces these various approaches to allow students and researchers to appreciate the techniques, and the considerations that underpin their choice, for modelling trends and cycles.

Introduction
'Classical' Techniques of Modelling Trends and Cycles
Stochastic Trends and Cycles
Filtering Economic Time Series
Nonlinear and Nonparametric Trend Modelling
Multivariate Modelling of Trends and Cycles
Conclusions
References
Index

TERENCE C. MILLS is Professor of Applied Statistics and Econometrics and the Head of the department of Economics at Loughborough University. He previously held professorial appointments at City University business school and Hull. He is the author of Time Series Techniques for Economists and The Econometric Modelling of Financial Time Series, plus over one hundred articles in journals and books.