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Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis
Author(s) -
Hassani Hossein,
Heravi Saeed,
Zhigljavsky Anatoly
Publication year - 2013
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2244
Subject(s) - singular spectrum analysis , autoregressive model , industrial production , multivariate statistics , autoregressive integrated moving average , series (stratigraphy) , predictability , econometrics , production (economics) , causality (physics) , time series , granger causality , computer science , statistics , mathematics , economics , artificial intelligence , macroeconomics , paleontology , physics , quantum mechanics , singular value decomposition , biology
In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was 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 autoregressive integrated moving average and vector autoregressive models. We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series. Copyright © 2012 John Wiley & Sons, Ltd.