Presents original and up-to-date studies in unobserved components (UC) time series models from both theoretical and methodological perspectives.

This volume presents original and up-to-date studies in unobserved components (UC) time series models from both theoretical and methodological perspectives. It also presents empirical studies where the UC time series methodology is adopted. Drawing on the intellectual influence of Andrew Harvey, the work covers three main topics: the theory and methodology for unobserved components time series models; applications of unobserved components time series models; and time series econometrics and estimation and testing. These types of time series models have seen wide application in economics, statistics, finance, climate change, engineering, biostatistics, and sports statistics. The volume effectively provides a key review into relevant research directions for UC time series econometrics and will be of interest to econometricians, time series statisticians, and practitioners (government, central banks, business) in time series analysis and forecasting, as well to researchers and graduate students in statistics, econometrics, and engineering.

A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.

The focus of the global economy has increasingly shifted toward China and emerging countries. However, despite their high growth prospects, emerging economies often lack the sound capital market and corporate governance systems necessary to promote the efficient allocation of financial resources to maintain the confidence of capital providers. As China becomes more prominent economically, the development of its capital market becomes an increasingly important issue. This book presents some of the latest academic research on China's capital markets, demonstrating some of the major issues currently being faced. Preeminent researchers in the field examine key topics such as the performance of commercial banks, dividends and ownership, financial constraints and firm performance, the role of political networks, stock price decomposition, stock return predictability, and the role of media coverage. In this book, the authors use the country's institutional background to offer useful insight into policy implications for the development of China as well as other emerging economies.

This book comprises the articles of the 6th Econometric Workshop in Karlsruhe, Germany. In the first part approaches from traditional econometrics and innovative methods from machine learning such as neural nets are applied to financial issues. Neural Networks are successfully applied to different areas such as debtor analysis, forecasting and corporate finance. In the second part various aspects from Value-at-Risk are discussed. The proceedings describe the legal framework, review the basics and discuss new approaches such as shortfall measures and credit risk.

Economics: Beyond the Millennium contains articles by leading authorities in various fields of economic theory and econometrics, each of whom gives an account of the current state of the art in their own field and indicate the direction that they think it will take in the next ten years. The fields covered are grouped into three categories: the microfoundations of macroeconomics, where Malinvaud evaluates the theory of resource allocation and Hildenbrand examines the empirical content of economic thories; markets and and organizations, where both Gabszewicz and D'Aspremont et al. look at imperfect competition and general equilibrium, Scotchmer and Thiess consider spatial economics, Ponssard the future of managerial economics, while Van Damme looks at the next stage of game theory; and econometrics, where Gourieroux reviews econometric modelling in general, Maravall looks at time series, Lubrand and Bauwens examine Bayesian analysis, and Blundell looks at the rapidly expanding area of microeconometrics.

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

The approach to many problems in economic analysis has changed drastically with the development and dissemination of new and more efficient computational techniques. Computational Economic Systems: Models, Methods & Econometrics presents a selection of papers illustrating the use of new computational methods and computing techniques to solve economic problems. Part I of the volume consists of papers which focus on modelling economic systems, presenting computational methods to investigate the evolution of behavior of economic agents, techniques to solve complex inventory models on a parallel computer and an original approach for the construction and solution of multicriteria models involving logical conditions. Contributions to Part II concern new computational approaches to economic problems. We find an application of wavelets to outlier detection. New estimation algorithms are presented, one concerning seemingly related regression models, a second one on nonlinear rational expectation models and a third one dealing with switching GARCH estimation. Three contributions contain original approaches for the solution of nonlinear rational expectation models.

Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time s