Skip to main content
SpringerLink
Log in

Estimating multi-country prosperity index: A two-dimensional singular spectrum analysis approach

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

With the development of the global economy, interaction among different economic entities from one region is intensifying, which makes it significant to consider such interaction when constructing composite index for each country from one region. Recent advances in signal extraction and time series analysis have made such consideration feasible and practical. Singular spectrum analysis (SSA) is a well-developed technique for time series analysis and proven to be a powerful tool for signal extraction. The present study aims to introduce the usage of the SSA technique for multi-country business cycle analysis. The multivariate SSA (MSSA) is employed to construct a model-based composite index and the two dimensional SSA (2D-SSA) is employed to establish the multi-country composite index. Empirical results performed on Chinese economy demonstrate the accuracy and stability of MSSA-based composite index, and the 2D-SSA based composite indices for Asian countries confirm the efficiency of the technique in capturing the interaction among different countries.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Marcellino M and Mazzi G L, Introduction to advances in business cycle analysis and forecasting, Journal of Forecasting, 2010, 29: 1–5.

    Article  MathSciNet  Google Scholar 

  2. Marcellino M, Leading indicators, Handbook of Economic Forecasting (eds. by Elliott G, Granger C W J, and Timmermann A), 2006, 1: 879–960.

    Article  Google Scholar 

  3. Stock J H and Watson M W, New indexes of coincident and leading economic indicators, NBER Macroeconomic Annual (eds. by Blanchard O and Fischer S), MIT Press, 1989, 351–394.

    Google Scholar 

  4. Stock J H and Watson M W, A probability model of the coincident indicators, Leading Eco nomic Indicators: New Approaches And Forecasting Records (eds. by Lahiri K and Moore G H), Cambridge University Press, 1991, 63–90.

    Chapter  Google Scholar 

  5. Stock J H and Watson M W, Forecasting using principal components from a large number of predictors, Journal of the American Statistical Association, 2002, 97: 1167–1179.

    Article  MATH  MathSciNet  Google Scholar 

  6. Forni M, Hallin M, Lippi M, and Reichlin L, The generalized dynamic factor model: Identification and estimation, Review of Economics and Statistics, 2000, 82(4): 540–554.

    Article  Google Scholar 

  7. Forni M, Hallin M, Lippi M, and Reichlin L, The generalized dynamic factor model: Consistency and rates, Journal of Econometrics, 2004, 119(2): 231–255.

    Article  MathSciNet  Google Scholar 

  8. Artis M, Krolzig H M, and Toro J, The European business cycle, Oxford Economic Papers, 2004, 1–44.

    Google Scholar 

  9. Artis M J, Zenon G K, and Osborn D R, Business cycles for G7 and European countries, The Journal of Business, 1997, 70: 249–279.

    Article  Google Scholar 

  10. Fidrmuc J and Korhonen I, Meta-analysis of the business cycle correlation between the euro area and the CEECs, Journal of Comparative Economics, 2006, 34: 518–537.

    Article  Google Scholar 

  11. Forni M, Hallin M, Lippi M, and Reichlin L, Coincident and leading indicators for the Euro area, The Economic Journal, 2001, 111: 62–85.

    Article  Google Scholar 

  12. Golyandina N, Nekrutkin V, and Zhigljavsky A, Analysis of Time Series Structure: SSA and Related Techniques, Chapman & Hall/CRC, New York, London, 2001.

    Book  Google Scholar 

  13. Elsner J B and Tsonis A A, Singular Spectrum Analysis: A New Tool in Time Series Analysis, Plenum, 1996.

    Book  Google Scholar 

  14. Hassani H, Zokaei M, von Rosen D, Amiri S, and Ghodsi M, Does noise reduction matter for curve fitting in growth curve models?, Computer Methods and Program in Biomedicine, 2009, 96(3): 173–181.

    Article  Google Scholar 

  15. Hassani H, Heravi S, and Zhigljavsky A, Forecasting European industrial production with singular spectrum analysis, International Journal of Forecasting, 2009, 25(1): 103–118.

    Article  Google Scholar 

  16. Ghodsi M, Hassani H, Sanei S, and Hick Y, The use of noise information for detecting temporomandibular disorder, Biomedical Signal Processing and Control, 2009, 4: 79–85.

    Article  Google Scholar 

  17. Zhigljavsky A, Singular spectrum analysis for time series: Introduction to this special issue, Statistics and Its Interface, 2010, 3(3): 255–258.

    Article  MATH  MathSciNet  Google Scholar 

  18. Hassani H and Thomakos D, A review on singular spectrum analysis for economic and financial time series, Statistics and Its Interface, 2010, 3(3): 377–397.

    Article  MATH  MathSciNet  Google Scholar 

  19. Hassani H, Singular spectrum analysis: Methodology and comparison, Journal of Data Science, 2007, 5: 239–257.

    Google Scholar 

  20. Hassani H, Singular spectrum analysis based on the minimum variance estimator, Nonlinear Analysis: Real World Application, 2009, 11: 2065–2077.

    Article  MathSciNet  Google Scholar 

  21. Hassani H, Development of the theoretical and methodological aspects of the singular spectrum analysis and its application for analysis and forecasting of economics data, PHD Thesis, Cardiff University, 2009.

    Google Scholar 

  22. Hassani H, Dionisio A, and Ghodsi M, The effect of noise reduction in measuring the linear and nonlinear dependency of financial markets, Nonlinear Analysis: Real World Applications, 2010, 11: 492–502.

    Article  MathSciNet  Google Scholar 

  23. Hassani H, Zhigljavsky A, Patterson K, and Soofi A, A comprehensive causality test based on the singular spectrum analysis, Causality in Science, Oxford University press, Chapter 18, 2010, 379–404.

    Google Scholar 

  24. Golyandina N and Stepanov D, SSA-based approaches to analysis and forecast of multidimensional time series, Proceedings of the 5th St. Petersburg Workshop on Simulation (eds. by Ermakov S M, Melas V B, and Pepelyshev A N), St. Petersburg, 2005.

    Google Scholar 

  25. Patterson K, Hassani H, Heravi S, and Zhigljavsky A, Forecasting the final vintage of the industrial production series, Journal of Applied Statistics, 2010.

    Google Scholar 

  26. Lisi F and Medio A, Is a random walk the best exchange rate predictor? International Journal of Forecasting, 1997, 13: 255–267.

    Article  Google Scholar 

  27. Hassani H, Soofi A, and Zhigljavsky A, Predicting daily exchange rate with singular spectrum analysis, Nonlinear Analysis: Real World Applications, 2009, 11: 2023–2034.

    Article  MathSciNet  Google Scholar 

  28. Hassani H and Zhigljavsky A, Singular spectrum analysis: Methodology and application to economics data, Journal of System Science and Complexity, 2009, 22: 372–394.

    Article  MathSciNet  Google Scholar 

  29. Rodryguez-Aragon L and Zhigljavsky A, Singular spectrum analysis for image processing, Statistics and Its Interface, 2010, 3: 419–426.

    Article  MathSciNet  Google Scholar 

  30. Broomhead D S and King G, Extracting qualitative dynamics from experimental data, Physica D, 1986, 20: 217–236.

    Article  MATH  MathSciNet  Google Scholar 

  31. Plaut G and Vautard R, Spells of low-frequency oscillations and weather regimes in the Northern Hemisphere, J. Atmos. Sci., 1994, 51: 210–236.

    Article  MathSciNet  Google Scholar 

  32. Hassani H, and Mahmoudvand R, Multivariate singular spectrum analysis: A general view and new vector forecasting approach, International Journal of Energy and Statistics, 2013, 1(1): 55–83.

    Article  Google Scholar 

  33. Golyandina N and Usevich K, 2D-extension of Singular Spectrum 151 Analysis: Algorithm and elements of theory, Matrix Methods: Theory, Algo-152 rithms, Applications (eds. by Olshevsky V and Tyrtyshnikov E), World Scientific Publishing, 2010, 450–474.

  34. Rua A and Nunes L, Coincident and leading indicators for the euro area: A frequency band approach, International Journal of Forecasting, 2007, 21: 503–523.

    Article  Google Scholar 

  35. Anirvan B and Lorene H, A framework for measuring international business cycles, International Journal of Forecasting, 2001, 17: 333–348.

    Article  Google Scholar 

  36. The Conference Board, Business Cycle Indicators Handbook, New York, 2001.

  37. Nilsson R and Brunet O, Composite Leading Indicators for Major OECD Non-Member Economies: Brazil, China, India, Indonesia, Russian Federation, South Africa, OECD Statistics Working Papers, 2006.

    Google Scholar 

  38. Golyandina N, Usevich K, and Florinsky I, Filtering of digital terrain models by two-dimensional singular spectrum analysis, International Journal of Ecology & Development, 2007, 87: 81–94.

    Google Scholar 

  39. Atabek A, Evren E, and Saygin S, A new composite leading indicator for turkish economic activity, Emerging Markets Finance and Trade, 2005, 41: 45–64.

    Google Scholar 

  40. Jogen B P, Composite leading indicator for the Austrian economy, WIFO Working Papers, 2010.

    Google Scholar 

  41. Binner J M, Bissoondeeal R K, and Mullineux A W, Composite leading indicator of the inflation cycle for the Euro area, Applied Economics, 2005, 37: 1257–1266.

    Article  Google Scholar 

  42. Victor Z and Ataman O, Time series decomposition and measurement of business cycles, trends and growth cycles, Journal of Monetary Economics, 2006, 53: 1717–1739.

    Article  Google Scholar 

  43. Bandholz H and Funke M, In search of leading indicators of economic activity in Germany, Journal of Forecasting, 2003, 22: 277–297.

    Article  Google Scholar 

  44. Burns A F and Mitchell W C, Measuring business cycles, National Bureau of Economic Research, 1946.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Jiawei Zhang

  2. The Business School, Bournemouth University, 89 Holdenhurst Road, BH88EB, Poole, UK

    Hossein Hassani

  3. Research Center of Applied Finance, University of International Business and Economics, Beijing, 100029, China

    Haibin Xie

  4. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Xun Zhang

Authors
  1. Jiawei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Hassani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haibin Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xun Zhang.

Additional information

This research is supported by the National Science Foundation of China under Grant No. 71101142 and Presidential Award of Chinese Academy of Sciences.

This paper was recommended for publication by Editor WANG Shouyang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, J., Hassani, H., Xie, H. et al. Estimating multi-country prosperity index: A two-dimensional singular spectrum analysis approach. J Syst Sci Complex 27, 56–74 (2014). https://doi.org/10.1007/s11424-014-3314-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-014-3314-3

Keywords

Search

Navigation