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"The recent financial crisis has generated many structural changes within the economy. Many issues are ongoing, and the question of how to recover from the crisis, and how to avoid another one, are continually addressed by scholars and practitioners everywhere. Where there is much discussion within academic and practitioner circles, there is not always adequate interaction between these schools of research. This book provides a thorough overview of the recent financial crisis from the perspective of both industry practitioners and academics specialising in the area. The first part provides practitioner insight on the crisis, and explores the causes and effects and of the recession, European public financing, ECB monetary policy and the Euro, the repression of financial markets, and financial stability. Part two focuses on the case of Greece, as a country still heavily impacted by the crisis, which has undergone various unorthodox policies imposed by the IMF, the ECB the EU. The third part provides insight from researchers and academics, covering an array of Economic theories and revealing new economics architectures available for the future. With informed views from both financial industry practitioners and academics, this book discusses current issues and implementable solutions for a faster post-crisis recovery"--

The first book to be published on the Theta method, outlining under what conditions the method outperforms other forecasting methods This book is the first to detail the Theta method of forecasting - one of the most difficult-to-beat forecasting benchmarks, which topped the biggest forecasting competition in the world in 2000: the M3 competition. Written by two of the leading experts in the forecasting field, it illuminates the exact replication of the method and under what conditions the method outperforms other forecasting methods. Recent developments such as multivariate models are also included, as are a series of practical applications in finance, economics, and healthcare. The book also offers practical tools in MS Excel and guidance, as well as provisional access, for the use of R source code and respective packages. Forecasting with the Theta Method: Theory and Applications includes three main parts. The first part, titled Theory, Methods, Models & Applications details the new theory about the method. The second part, Applications & Performance in Forecasting Competitions, describes empirical results and simulations on the method. The last part roadmaps future research and also include contributions from another leading scholar of the method - Dr. Fotios Petropoulos. First ever book to be published on the Theta Method Explores new theory and exact conditions under which methods would outperform most forecasting benchmarks Clearly written with practical applications Employs R - open source code with all included implementations Forecasting with the Theta Method: Theory and Applications is a valuable tool for both academics and practitioners involved in forecasting and respective software development.

"This book offers a comprehensive guide to modern day taxation issues. It presents a thorough overview of many of the crucial aspects of applied taxation and current tax systems, and presents evidence that supports taxation as an important policy issue requiring immediate address globally. Contributions seek to address the core question of how to design a tax policy mix that can serve primarily efficiency, growth and possibly equity goals at a time where fiscal spending, for many economies, is not a viable option. Chapters provide a historical perspective on taxation, then go on to cover aspects of the modern theory of optimal taxation and tax design and provide valuable international perspectives on current tax practices and much required tax reforms. Empirical analysis on taxation and related economic data help the readers to understand how data-based observations and results are linked to the theory of taxation, and more importantly economic growth, before offering appropriate policy prescriptions. This book will be of interest to scholars and practitioners interested in learning more about taxation and why it matters today in the global economy."--

We review the Supply Chain (SC) planning in Defence operations. Our main focus is an exploration of the literature to identify the significant factors concerning SC planning and the way they affect the implementation of Decision Making Processes (DMP) in (primarily) military operations. We take as our starting point the logistics of Alexander the Great's army, to identify the key factors that led to his legendary success and to understand how these factors influenced his strategic decisions. We then review the practices, and evaluate the effectiveness, of countries and international bodies that have already implemented such SC management and planning systems to assist in their defence activities' decisions. Then, we illustrate the literature review with specific modern day examples of military deployment. Although many countries have invested in IT systems which may provide up to date information and cost-effectiveness suggestions, nevertheless, the utility of these systems in the defence sector, seems to be often surrounded by emotions and private or vested interests. Our research thus far suggests that there are substantial benefits to be gained by adopting a relevant decision framework to be implemented among military activities. At the end we attempt an identification of the key factors concerning SC planning since Alexander's time and their effectiveness level in recent defence operations among other existing DMP tools.

In this paper we present several new ¯ndings on the NoVaS transformation approach for volatility forecasting introduced by Politis (2003a,b, 2007). In particular: (a) we present a new method for accurate volatility forecasting using NoVaS ; (b) we introduce a \time- varying" version of NoVaS and show that the NoVaS methodology is applicable in situations where (global) stationarity for returns fails such as the cases of local stationarity and/or structural breaks and/or model uncertainty; (c) we conduct an extensive simulation study on the forecasting ability of the NoVaS approach under a variety of realistic data generating processes (DGP); and (d) we illustrate the forecasting ability of NoVaS on a number of real datasets and compare it to realized and range-based volatility measures. Our empirical results show that the NoVaS -based forecasts lead to a much `tighter' distribution of the forecasting performance measure. Perhaps our most remarkable ¯nding is the robustness of the NoVaS forecasts in the context of structural breaks and/or other non-stationarities of the underlying data. Also striking is that forecasts based on NoVaS invariably outperform those based on the benchmark GARCH(1,1) even when the true DGP is GARCH(1,1) when the sample size is moderately large, e.g. 350 daily observations.

The article reports results of an empirical investigation into trade, openness, and domestic conflict for several Latin American countries. It addresses two main issues: (1) whether variations in trade openness affect the likelihood of the onset of domestic conflict and (2) once initiated, how variations in openness affect conflict duration. For the period 1973- 1995, and controlling for numerous sociopolitical, institutional, and economic factors, our findings suggest that (1) increased trade openness reduces the chance for domestic conflict onset as well as the intensity of domestic conflict and (2) over-reliance on agricultural exports, which can be a consequence of increased openness, is the main factor sustaining conflict. Conflict mitigation policies should keep in mind the role tradable agricultural goods play in this region of the world.

ABSTRACT: Medical tourism is considered nowadays as a multi-billion industry which can promote a country's economic growth. Therefore, forecasting the scheduled tourism demand for medical services is of great importance for policy makers. Doing so, however, is not an easy task due to the following reasons: Data on medical tourism are (i) not easily accessible; (ii) not typically distinguished from tourists' non-scheduled (unintentional) use of a country's medical services; and (iii) usually not publicly available for long time periods. In this paper, we present a novel way to forecast tourism demand (intentional and unintentional) foro medical services —a rough but informative proxy of medical tourism— using limited data. To perform the analysis, we use data on the percentage of hospital discharges of non-residents for 17 European countries over the period 2008-2019 retrieved from Eurostat. Our methodological approach is based on a forecasting technique recently developed by Kyriazi, Thomakos and Guerard ; the adaptive learning forecasting. According to this method, MSE (Mean Squared Error)-performance enhancements can be achieved using any forecast as input —as long as that input is not a 'perfect' forecast— by learning from past forecast errors. Within this context, even the most basic forecast, the no-change or naïve forecast, can be used as input to the adaptive learning procedure. Kyriazi, Thomakos and Guerard approach is very well suited to our research question because (i) the no-change forecast is a natural candidate in a short time series where models cannot be estimated with sufficient accuracy, (ii) the no-change forecast is obviously far from being the 'perfect' forecast, and (iii) the adaptive learning process can be initialized by the no-change forecast and then learn by its own past forecast errors. Our results show that adaptive learning forecasting leads to performance enhancements that range from the 5% to more than 20% relative to the no-change benchmark. This finding indicates the efficiency of the adaptive learning method in forecasting medical tourism demand; an important subcategory of tourism demand for which data are not easily accessible and freely available historical data are existing for short time periods.