Nonlinear Time Series Analysis of Business Cycles, 276
- Costas Milas, Keele University, Keele, UK
- Philip Rothman, East Carolina University, Greenville, NC, USA
- Dick van Dijk, Erasmus University Rotterdam, Rotterdam, The Netherlands
The business cycle has long been the focus of empirical economic research. Until recently statistical analysis of macroeconomic fluctuations was dominated by linear time series methods. Over the past 15 years, however, economists have increasingly applied tractable parametric nonlinear time series models to business cycle data; most prominent in this set of models are the classes of Threshold AutoRegressive (TAR) models, Markov-Switching AutoRegressive (MSAR) models, and Smooth Transition AutoRegressive (STAR) models. In doing so, several important questions have been addressed in the literature, including:
1. Do out-of-sample (point, interval, density, and turning point) forecasts obtained with nonlinear time series models dominate those generated with linear models?
2. How should business cycles be dated and measured?
3. What is the response of output and employment to oil-price and monetary shocks?
4. How does monetary policy respond to asymmetries over the business cycle?
5. Are business cycles due more to permanent or to transitory negative shocks?
6. Is the business cycle asymmetric, and does it matter?
Accordingly, we have compiled and edited a book for the Elsevier economics program comprising 15 original papers on these and related themes.
Economists: Academics, Professionals and Students