Time series analysis in econometrics
Members of Smu, in collaboration with colleagues in Cardiff Business School and other universities, have done a lot of research on the application of singular spectrum analysis (see SSA: theory and methodology) for the analysis and forecasting of economics, business and finance time series. Examples of econometric time series analysed include European Industrial production series [1,2], various GDP series [1-3] and inflation indices [4].
Special emphasis is paid to the analysis of structural stability of the series, to the of multivariate series and to the detection of causality between the series [2]. Multivariate SSA has also been applied to the analysis of the exchange rates in [5] where it was shown that despite individual exchange rate series do not have any detectable structure, there are very clear patterns in the cross-dependence between the exchange rate series.
The list of selected publications reflects our recent work in this area.
Selected publications
- Hassani H., Heravi S., Zhigljavsky A. (2009) Forecasting European Industrial Production with Singular Spectrum Analysis, International Journal of Forecasting, 25, No. 1, p. 103-118.
- Hassani, H; Zhigljavsky, A; Patterson, K; Soofi, A. (2011). A Comprehensive Causality Test Based on the Singular Spectrum Analysis, Causality in Science (eds. P. M. Illari, F. Russo and J. Williamson), Oxford University press, 379-404.
- Hassani H., Zhigljavsky A.(2009) Singular Spectrum Analysis: Methodology and Application to Economics Data, Journal of Systems Science and Complexity, v. 22, No. 3, p. 372-394.
- Patterson K., Hassani H., Heravi S., Zhigljavsky A. (2011) Multivariate singular spectrum analysis for forecasting revisions to real-time data, Journal of Applied Statistics, v. 38, No. 10, 2183-2211
- Hassani H., Soofi A., Zhigljavsky A. (2010) Predicting Daily Exchange Rate with Singular Spectrum Analysis Data, Nonlinear Analysis: Real World Applications, v. 11 No. 3, 2023—2034