From the Back Cover
Applied Time Series Modelling and Forecasting
provides a relatively non–technical introduction to applied time series econometrics and forecasting involving non–stationary data. The emphasis is very much on the why
and, as much as possible, the authors confine technical material to boxes or point to the relevant sources for more detailed information.
This book is based on an earlier title Using Cointegration Analysis in Econometric Modelling by Richard Harris. As well as updating material covered in the earlier book, there are two major additions involving panel tests for unit roots and cointegration and forecasting of financial time series. Harris and Sollis have also incorporated as many of the latest techniques in the area as possible including: testing for periodic integration and cointegration; GLS detrending when testing for unit roots; structural breaks and season unit root testing; testing for cointegration with a structural break; asymmetric tests for cointegration; testing for super–exogeniety; seasonal cointegration in multivariate models; and approaches to structural macroeconomic modelling. In addition, the discussion of certain topics, such as testing for unique vectors, has been simplified.
Applied Time Series Modelling and Forecasting has been written for students taking courses in financial economics and forecasting, applied time series, and econometrics at advanced undergraduate and postgraduate levels. It will also be useful for practitioners who wish to understand the application of time series modelling e.g. financial brokers.
Data sets and econometric code for implementing some of the more recent procedures covered in the book can be found on the following web site www.wiley.co.uk/harris
About the Author
is a Professor in the Department of Economics and Finance at the University of Durham. His areas of research are in the field of applied econometrics and he has published widely in numerous journals.
Robert Sollis is a Lecturer in the Department of Economics and Finance at the University of Durham. His research interests are in time series econometrics with particular focus on nonlinear models for macroeconomic and financial time series.