In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Effects of changes in stock index compositions: A literature survey

The long-term performance of index additions and deletions: Evidence from the Hang Seng Index☆

Corporate future investments and stock liquidity: Evidence from emerging markets

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Financial constraints, stock liquidity, and stock returns

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Index revisions, systematic liquidity risk and the cost of equity capital

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Information efficiency changes following FTSE 100 index revisions

The study of financial literacy on business students aims to highlight the financial literacy level of senior students of bachelor degree and postgraduate students.. Although the study illustrates how financial literacy associated with socio- demographic characteristics, financial education, financial behavior, family background, and social capital. The study employs mixed research methodology in which an administered questionnaire and focus group tools are used. The study finds that students’ interest for obtaining financial information is very low which reflects a low level of financial literacy. Although, theresults show a critical red flag of warning in understanding the basic financial concepts (inflation, simple interest rate calculation and portfolio diversification). Moreover, the results find that family background and financial socialization play a significant role in shaping family members’ financial literacy weather intentionally or spontaneously. Based on theresults of the analysis and focus group justification, theresearcher recommends that there is an insisting need for; enhancing the role of the universities in improving financial literacy, and recommends future studies to expand the range of study in light of the role of high education ministry, universities and financial institutions.

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Wael Daya

Papers

Index revisions, systematic liquidity risk and the cost of equity capital

Information efficiency changes following FTSE 100 index revisions

Assessment of financial literacy: case study of business students