Nonlinear Time Series Analysis of Business CyclesC. Milas, P. A. Rothman, Dick van Dijk, David E. Wildasin 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: Do out-of-sample (point, interval, density, and turning point) forecasts obtained with nonlinear time series models dominate those generated with linear models? How should business cycles be dated and measured? What is the response of output and employment to oil-price and monetary shocks? How does monetary policy respond to asymmetries over the business cycle? Are business cycles due more to permanent or to transitory negative shocks? And, is the business cycle asymmetric, and does it matter? "Contributions to Economic Analysis" was established in 1952. The series purpose is to stimulate the international exchange of scientific information. The series includes books from all areas of macroeconomics and microeconomics. |
Contents
body | 1 |
Forecasting US Recession Probabilities and Output Growth | 55 |
3 The Importance of Nonlinearity in Reproducing Business Cycle Features | 75 |
4 The Vector Floor and Ceiling Model | 97 |
5 A New Framework to Analyze Business Cycle Synchronization | 133 |
6 Nonlinearity and Instability in the Euro Area | 151 |
7 Nonlinear Modelling of Autoregressive Structural Breaks in Some US Macroeconomic Series | 175 |
Evidence from US Economic Time Series | 199 |
10 Random Walk Smooth Transition Autoregressive Models | 247 |
11 Nonlinearity and Structural Change in Interest Rate Reaction Functions for the US UK and Germany | 283 |
Some New Evidence for the EuroArea | 311 |
Norway 18302003 | 333 |
14 A Predictive Comparison of Some Simple Long and Short Memory Models of Daily US Stock Returns with Emphasis on Business Cycle Effects | 379 |
15 Nonlinear Modeling of the Changing Lag Structure in US Housing Construction | 407 |
| 431 | |
9 Modeling Inflation and Money Demand Using a FourierSeries Approximation | 221 |
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Common terms and phrases
algorithm analysis ARFC0a ARFIMA model ARMA asymmetries autoregressive models Bayes factors Bayesian business cycle business cycle features coefficients cointegrating critical values day ahead density distribution dynamics Economic effects empirical equation error estimation evaluation expansion expansion expansion Figure filtered probabilities forecast frequency GDP growth Granger growth rates inflation intercept interest rate Kalman filter leading indicator linear models LSTAR macroeconomic Markov Markov-switching models methods monetary policy monthly msfe multivariate NBER nonlinear nonlinear models Note null hypothesis observations output growth p-value panel paper parameters period Phillips curve prediction prior real exchange rate real money real-time recession recursive regime switching regression residuals RW-STAR models RWAR S₁ sample Section shocks simulated smooth transition smoothed probabilities specification STAR models structural breaks Table Taylor rule Teräsvirta threshold time-varying transition function transition variable trend TV-STAR variance vector VFC model y₁