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Signal Extraction and Forecasting of the UK Tourism Income Time Series. A Singular Spectrum Analysis Approach

Author

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  • Beneki, Christina
  • Eeckels, Bruno
  • Leon, Costas

Abstract

We present and apply the Singular Spectrum Analysis (SSA), a relatively new, non-parametric and data-driven method used for signal extraction (trends, seasonal and business cycle components) and forecasting of the UK tourism income. Our results show that SSA outperforms slightly SARIMA and time-varying parameter State Space Models in terms of RMSE, MAE and MAPE forecasting criteria.

Suggested Citation

  • Beneki, Christina & Eeckels, Bruno & Leon, Costas, 2009. "Signal Extraction and Forecasting of the UK Tourism Income Time Series. A Singular Spectrum Analysis Approach," MPRA Paper 18354, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:18354
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    References listed on IDEAS

    as
    1. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
    2. Leon, Costas & Eeckels, Bruno, 2009. "A Dynamic Correlation Approach of the Swiss Tourism Income," MPRA Paper 15215, University Library of Munich, Germany.
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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