23 February 2010
Abstract: Nonlinear models have been widely employed to characterize the underlying structure in a time series. It has been shown that the in-sample fit of nonlinear models is better than linear models, however, the superiority of nonlinear models over linear models, from the perspective of out-of-sample forecasting accuracy remains doubtful. We compare forecast accuracy of nonlinear regime switching models against classical linear models using different performance scores, such as root mean square error (RMSE), mean absolute error (MAE), and the continuous ranked probability score (CRPS). We propose and investigate the efficacy of a class of simple nonparametric, nonlinear models that are based on estimation of a few parameters, and can generate more accurate forecasts when compared with the classical models. Also, given the importance of gauging uncertainty in forecasts for proper risk assessment and well informed decision making, we focus on generating and evaluating both point and density forecasts. Keywords: Nonlinear, Forecasting, Performance scores.
- Junior Applied Mathematics Seminar