This research studies possible existence of business cycle asymmetries in Canada, France, Germany, Italy, Japan, UK, and US real GDP growth rates. Asymmetries in these countries are modeled using in-sample as well as jackknife out-of-sample forecasts approximated from artificial neural networks....

In this study we employ augmented and switching time series models to find possible existence of business cycle asymmetries in U.S. stock returns. Our approach is fully parametric and testing strategy is robust to any conditional heteroskedasticity, and outliers that may be present. We also...

We employ artificial neural networks using macro-financial variables to predict recessions. We model the relationship between indicator variables and recessions to periods into the future and employ a procedure that penalizes a misclassified recession more than a misclassified non-recession. Our...

We investigate whether business cycle dynamics in seven industrialized countries (the G7) are characterized by asymmetries in conditional mean. We provide evidence on this issue using a variety of time series models. Our approach is fully parametric. Our testing strategy is robust to any...

We investigate business cycle asymmetries in the real GDP of eleven selected Asian economies using nonlinear switching time series models and artificial neural networks. Results based on neural network linearity tests show evidence of business cycle asymmetries in all series. Results based on...