Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis

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.

[1]  Emmanuel Sirimal Silva,et al.  Forecasting the price of gold , 2015 .

[2]  Kerry Patterson,et al.  A Comprehensive Causality Test Based on the Singular Spectrum Analysis , 2011 .

[3]  Anatoly Zhigljavsky,et al.  Predicting daily exchange rate with singular spectrum analysis , 2010 .

[4]  Hossein Hassani,et al.  The effect of noise reduction in measuring the linear and nonlinear dependency of financial markets , 2010 .

[5]  Dietrich von Rosen,et al.  Does noise reduction matter for curve fitting in growth curve models? , 2009, Comput. Methods Programs Biomed..

[6]  Anatoly A. Zhigljavsky,et al.  Singular spectrum analysis: methodology and application to economics data , 2009, J. Syst. Sci. Complex..

[7]  Saeid Sanei,et al.  The use of noise information for detection of temporomandibular disorder , 2009, Biomed. Signal Process. Control..

[8]  A. Zhigljavsky,et al.  Forecasting European industrial production with singular spectrum analysis , 2009 .

[9]  J. Doornik,et al.  An Omnibus Test for Univariate and Multivariate Normality , 2008 .

[10]  H. White,et al.  A NONPARAMETRIC HELLINGER METRIC TEST FOR CONDITIONAL INDEPENDENCE , 2008, Econometric Theory.

[11]  Cees Diks,et al.  A new statistic and practical guidelines for nonparametric Granger causality testing , 2006 .

[12]  Cees Diks,et al.  A Note on the Hiemstra-Jones Test for Granger Non-causality , 2005 .

[13]  C. Glymour,et al.  Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms , 2005 .

[14]  Rui Menezes,et al.  Mutual information: a measure of dependency for nonlinear time series , 2004 .

[15]  Saeed Heravi,et al.  Linear versus neural network forecasts for European industrial production series , 2004 .

[16]  V. Moskvina,et al.  An Algorithm Based on Singular Spectrum Analysis for Change-Point Detection , 2003 .

[17]  Andrew L. Rukhin,et al.  Analysis of Time Series Structure SSA and Related Techniques , 2002, Technometrics.

[18]  Abdol S. Soofi,et al.  Nonlinear Forecasting of Noisy Financial Data , 2002 .

[19]  Cees Diks,et al.  A general nonparametric bootstrap test for Granger causality , 2001 .

[20]  G. Darbellay,et al.  The entropy as a tool for analysing statistical dependences in financial time series , 2000 .

[21]  Abdol S. Soofi,et al.  Nonlinear deterministic forecasting of daily dollar exchange rates , 1999 .

[22]  Saeed Heravi,et al.  Seasonal unit roots and forecasts of two-digit European industrial production , 1999 .

[23]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[24]  H. Luschgy.  Nonparametric Statistics for Stochastic Processes - D. Bosq. , 1998 .

[25]  Paul Newbold,et al.  Testing the equality of prediction mean squared errors , 1997 .

[26]  The Accuracy of OECD Forecasts for Japan , 1997 .

[27]  Michael P. Clements,et al.  A Monte Carlo Study of the Forecasting Performance of Empirical Setar Models , 1999 .

[28]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[29]  J. Elsner,et al.  Singular Spectrum Analysis: A New Tool in Time Series Analysis , 1996 .

[30]  Denis Bosq,et al.  Nonparametric statistics for stochastic processes , 1996 .

[31]  Craig Hiemstra,et al.  Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation , 1994 .

[32]  C. Granger,et al.  USING THE MUTUAL INFORMATION COEFFICIENT TO IDENTIFY LAGS IN NONLINEAR MODELS , 1994 .

[33]  G. Plaut,et al.  Spells of Low-Frequency Oscillations and Weather Regimes in the Northern Hemisphere. , 1994 .

[34]  Clive W. J. Granger,et al.  Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests , 1993 .

[35]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[36]  David Hsieh.  Chaos and Nonlinear Dynamics: Application to Financial Markets , 1991 .

[37]  Helmut Lütkepohl,et al.  Introduction to multiple time series analysis , 1991 .

[38]  H. White.  Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models , 1989 .

[39]  Y. Hochberg.  A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[40]  R. D'Agostino,et al.  Goodness-of-Fit-Techniques , 1987 .

[41]  R. Tsay.  Nonlinearity tests for time series , 1986 .

[42]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[43]  A. I. McLeod,et al.  DIAGNOSTIC CHECKING ARMA TIME SERIES MODELS USING SQUARED‐RESIDUAL AUTOCORRELATIONS , 1983 .

[44]  W. Grove.  Statistical Methods for Rates and Proportions, 2nd ed , 1981 .

[45]  C. Granger.  Testing for causality: a personal viewpoint , 1980 .

[46]  Michael D. Geurts,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[47]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[48]  J. B. Ramsey,et al.  Tests for Specification Errors in Classical Linear Least‐Squares Regression Analysis , 1969 .