On the use of Singular Spectrum Analysis for Forecasting U.S. Trade before, during and after the 2008 Recession

This paper is aimed at introducing the powerful, nonparametric time series analysis andforecasting technique of Singular Spectrum Analysis (SSA) for trade forecasting via an application which evaluates the impact of the 2008 recession on U.S. trade forecasting models.This research is felicitous given the magnitude of the structural break visible in the U.S.trade series following the 2008 economic crisis. Structural breaks resulting from such recessions might affect conclusions from traditional unit root tests and forecasting models whichmakes use of these tests. As such, it is prudent to evaluate the sensitivity and reliability ofparametric, historical trade forecasting models in comparison to the relatively modern, nonparametric models. In doing so, we introduce the SSA technique for trade forecasting andperform exhaustive statistical tests on the data for normality, stationarity and change points,and the forecasting results for statistical significance prior to reaching the well-founded conclusion that SSA is less sensitive to the impact of recessions on U.S. Trade, in comparisonto an optimized ARIMA model, Exponential Smoothing and Neural Network models. Ergo,we conclude that SSA is able to provide more accurate forecasts for U.S. Trade in the face ofrecessions, and is therefore presented