Financial econometrics is one of the greatest on-going success stories of recent decades, as it has become one of the most active areas of research in econometrics. In this book, Michael Clements presents a clear and logical explanation of the key concepts and ideas of forecasts of economic and financial variables. He shows that forecasts of the single most likely outcome of an economic and financial variable are of limited value. Forecasts that provide more information on the expected likely ranges of outcomes are more relevant. This book provides a comprehensive treatment of the evaluation of different types of forecasts and draws out the parallels between the different approaches. It describes the methods of evaluating these more complex forecasts which provide a fuller description of the range of possible future outcomes.

Financial econometrics is one of the greatest on-going success stories of recent decades, as it has become one of the most active areas of research in econometrics. In this book, Michael Clements presents a clear and logical explanation of the key concepts and ideas of forecasts of economic and financial variables. He shows that forecasts of the single most likely outcome of an economic and financial variable are of limited value. Forecasts that provide more information on the expected likely ranges of outcomes are more relevant. This book provides a comprehensive treatment of the evaluation of different types of forecasts and draws out the parallels between the different approaches. It describes the methods of evaluating these more complex forecasts which provide a fuller description of the range of possible future outcomes.

Following theseminal Palgrave Handbook of Econometrics: Volume I , this second volume brings together the finestacademicsworking in econometrics today andexploresapplied econometrics, containing contributions onsubjects includinggrowth/development econometrics and applied econometrics and computing.

This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.

This book provides an introductory treatment of time series econometrics, a subject that is of key importance to both students and practitioners of economics. It contains material that any serious student of economics and finance should be acquainted with if they are seeking to gain an understanding of a real functioning economy.

This rigorous textbook introduces graduate students to the principles of econometrics and statistics with a focus on methods and applications in financial research. Financial Econometrics, Mathematics, and Statistics introduces tools and methods important for both finance and accounting that assist with asset pricing, corporate finance, options and futures, and conducting financial accounting research. Divided into four parts, the text begins with topics related to regression and financial econometrics. Subsequent sections describe time-series analyses; the role of binomial, multi-nomial, and log normal distributions in option pricing models; and the application of statistics analyses to risk management. The real-world applications and problems offer students a unique insight into such topics as heteroskedasticity, regression, simultaneous equation models, panel data analysis, time series analysis, and generalized method of moments. Written by leading academics in the quantitative finance field, allows readers to implement the principles behind financial econometrics and statistics through real-world applications and problem sets. This textbook will appeal to a less-served market of upper-undergraduate and graduate students in finance, economics, and statistics. ​

The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics

An accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation-based tests to help perform econometric analyses.