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Title: Development of the theoretical and methodological aspects of the singular spectrum analysis and its application for analysis and forecasting of economics data
Author: Hassani, Hossein
ISNI:       0000 0004 2748 5829
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2009
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In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. The aim of this research is to develop theoretical and methodological aspects of the SSA technique and to demonstrate that SSA can be considered as a powerful method of time series analysis and forecasting, particulary for economic time series. For practical aspect and empirical results, various economic and financial time series are used. First, the SSA technique is applied as a noise reduction method. The performance of SSA is examined in noise reduction of several important financial series. The daily closing prices of several stock market indices are examined to analyse whether noise reduction matters in measuring dependencies of the financial series. The effect of noise reduction is considered on the linear and nonlinear measures of dependence between two series. The results are compared with those obtained with the linear and nonlinear methods for filtering time series. The results show that the performance of SSA is much better than of the competitive methods. Second, we consider the performance of SSA in forecasting various time series. For consistency with the forecasting results obtained with other current forecasting methods, the performance of the SSA technique is examined by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com pared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm. The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above. As another example, the performance of the SSA technique is assessed by applying it to 24 series measuring the monthly seasonally unadjusted industrial production for important sectors of the German, French and UK economies. The results confirm that at longer horizons, SSA significantly outperforms ARIMA and Holt-Winter methods. Moreover, the application of SSA to the analysis and forecasting of Iranian national accounts data, which are rather short, are considered to examine capability of SSA in forecasting short time series. The results confirm that SSA works very well for short time series as well as for long time series. The univariate and multivariate SSA are also employed in predicting the value and the changes in direction of inflation series for the United States. The consumer price indices, and real-time chain-weighted GDP price index series are used in these prediction exercises. Moreover, our out-of-sample 1-step-ahead moving prediction results are compared with the prediction results based on methods such as activity-based NAIRU Philips curve, AR(p), and random walk models with the latter as a naive forecasting method. A short-run (quarterly) and long-run (one to six years) time windows are utilized for predictions. The results clearly confirm that prediction of inflation rate in the United States during the period of "Great Moderation" is less challenging compared to more volatile inflationary period of 1970-1985 also. Furthermore, the univariate and multivariate SSA is used for predicting the value and the direction of changes in the daily pound/dollar exchange rate. Empirical results show that the forecast based on the multivariate SSA compares favorably to the forecast of the random walk model both for predicting the value and the direction of changes in the daily pound/dollar exchange rate. The SSA forecasting results are also compared to prediction results based on an error correction model (VEC) in the context of a restricted vector autoregressive model. The results show that the VEC results are inferior. For theoretical development of the technique, two new versions of SSA are introduced the SSA technique based on the minimum variance estimator and based on the perturbation theory. The new versions are examined in reconstructing and forecasting time series. The results are compared with the current version of SSA and indicate that the new versions improve the quality of reconstruction step as well as forecasting results. We also consider the concept of casual relationship between two time series based on the SSA technique. We introduce several criteria which characterize this causality. The criteria are based on the forecasting accuracy and predictability of the direction of change. The performance of the proposed test is examined using different real time series.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available