Before future-oriented information can be used as a basis for decision making in economics or business administration, it must be understood on a methodological level. This book provides decision makers with a thorough understanding of the possibilities offered by various forecasting methods as well as their limitations. If managers rely on a forecast with a long-term perspective to guide them in making short-term decisions, planning deficiencies will likely result. Likewise, if managers use short-term forecasts to inform their long-term strategic vision, failure could easily ensue. Graf provides the tools necessary to sidestep the common pitfall of using the wrong forecasting technique for the wrong purpose. This is not a detailed examination of the mathematical and statistical tools of empirical economic research. Instead, forecasting methods are explained so that they can be understood by the managers who employ them in their decision making. Graf demonstrates that understanding and—in special cases—cooperation between forecast developers and users is crucial to creating an effective forecast that results in informed management decisions. He discusses traditional, long-term, macroeconomic, and global economic forecasting; the scenario technique as a central instrument of long-term forecasting; and short-term economic and market forecasting.

A Companion to Economic Forecasting provides an accessible and comprehensive account of recent developments in economic forecasting. Each of the chapters has been specially written by an expert in the field, bringing together in a single volume a range of contrasting approaches and views. Uniquely surveying forecasting in a single volume, the Companion provides a comprehensive account of the leading approaches and modeling strategies that are routinely employed.

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

Section headings in this handbook include: 'Forecasting Methodology; 'Forecasting Models'; 'Forecasting with Different Data Structures'; and 'Applications of Forecasting Methods.'.

With the rapid development of economic globalization and information technology, the field of economic forecasting continues its expeditious advancement, providing business and government with applicable technologies. This book discusses various business intelligence techniques including neural networks, support vector machine, genetic programming, clustering analysis, TEI@I, fuzzy systems, text mining, and many more. It serves as a valuable reference for professionals and researchers interested in BI technologies and their practical applications in economic forecasting, as well as policy makers in business organizations and governments.

Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.

Economic Forecasting provides a comprehensive overview of macroeconomic forecasting. The focus is first on a wide range of theories as well as empirical methods: business cycle analysis, time series methods, macroeconomic models, medium and long-run projections, fiscal and financial forecasts, and sectoral forecasting.

This work is the only currently available text that provides comprehensive coverage of the methods and applications in the rapidly developing field of forecasting the future state of the economy.

Leisure Services Financial Management presents current knowledge, systems, and skills necessary for managing the financial operations of a leisure service agency, organization, or business. It covers a range of financial management models and approaches in public, private, and nonprofit sectors of the leisure service industry and contains a full set of ancillaries and an exclusive web resource.

Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.