Nonlinear and Nonparametric Modeling Approaches for Probabilistic Forecasting of the US Gross National Product

Numerous time series models are available for forecasting economic output. Autoregressive models were initially applied to US gross national product (GNP), and have been extended to complicated nonlinear structures, such as the self-exciting threshold autoregressive (SETAR) and Markov-switching autoregres-sive (MS-AR) models. This article proposes a parsimonious, nonlinear and nonpara-metric model that generates accurate point and density forecasts of US GNP. The out-of-sample forecast performance of the proposed model is found to be competi-tive compared with both previously published linear and nonlinear models for GNP time series. We validate our results on two post-war GNP time series using different 1 performance scores. Discover the world's research