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Forecasting European Industrial Production with Multivariate Singular Spectrum Analysis

In recent years the Singular Spectrum Analysis (SSA) technique, used as a powerful technique in time series analysis, has been developed and applied to many practical problems. The aim of this research is to develop theoretical and methodological aspects of the multivariate SSA (MSSA) technique and to demonstrate that MSSA can also be considered as a powerful method of time series analysis and forecasting. We use the UK Industrial Production series to illustrate the main findings. The performance of the SSA technique is assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the ARIMA and VAR models. We also develop the concept of casual relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria are based on the forecasting accuracy and the predictability of the direction of change. The performance of the proposed tests is examined using the same data, the UK industrial production series.