In recent years Singular Spectrum Analysis (SSA), a relatively novel but powerful technique in time series analysis, has been developed and applied to many practical problems across different fields. In this paper we review recent developments in the theoretical and methodological aspects of the SSA from the perspective of analyzing and forecasting economic and financial time series, and also represent some new results. In particular, we (a) show what are the implications of SSA for the, frequently invoked, unit root hypothesis of economic and financial times series; (b) introduce two new versions of SSA, based on the minimum variance estimator and based on perturbation theory; (c) discuss the concept of causality in the context of SSA; and (d) provide a variety of simulation results and real world applications, along with comparisons with other existing methodologies.

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For a "genuine" small open economy that has experienced both dictatorship and democracy, we find support for the predictions of the Grossman-Helpman (1994) "Protection for Sale" model. In contrast to previous studies, we use various protection measures (including tariffs, the direct measure of the theoretical model) and perform both single-year and panel regressions. Using Turkish industry-level data, the government's weight on welfare is estimated to be much larger than that on contributions. More importantly, we find that this weight is generally higher for the democratic regime than for dictatorship.

The cyclical properties of the Baltic Dry Index (BDI) and their implications for forecasting performance are investigated. We find that changes in the BDI can lead to permanent shocks to trade of major exporting economies. In our forecasting exercise, we show that commodities and trigonometric regression can lead to improved predictions and then use our forecasting results to perform an investment exercise and to show how they can be used for improved risk management in the freight sector.