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

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.

A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike

Gain the knowledge and skills that can help you exploit instability. No book can help you construct foolproof forecasting systems that will ensure you'll accurately predict economic turning points every time. But with Niemira and Klein's Forecasting Financial and Economic Cycles on hand, you'll be able to significantly strengthen your ability to measure, monitor, and forecast important fluctuations. Part history, it provides you with essential background material on the characteristics and causes of economic volatility. It offers accessible coverage of the classical business cycle, the five basic types of economic cycles as determined by leading economists, and evolving ideas on the forces driving instability—ranging from simple unicausal theories, more complex Keynesian theory, to new classical macroeconomics. In addition, its concise review of America's economic past highlights the lessons that can be learned from the various cycles experienced since shortly before World War II. Part handbook, Forecasting Financial and Economic Cycles presents the full spectrum of statistical techniques used to measure cycles, trends, seasonal patterns, and other vital changes, offering you step-by-step guidance on applying a specific method and detailing its uses and limitations. It goes on to show how youcan adapt particular techniques to assess, track, and predict: Industry cycles—including an objective, tailor-made forecasting tool Regional business cycles—including a survey of regional indicators International business cycles—with an international business cycle chronology Inflation cycles—plus "12 little-known facts" about this complex cycle Financial cycles—covering credit, monetary, and interest rate cycles Stock market cycles—with advice on achieving more disciplined trading Based on outstanding scholarship and years of practical experience, Forecasting Financial and Economic Cycles will serve as an invaluable tool for practitioners like you whose decision-making—and profit margin—depend on accurately assessing today's often uncertain economic climate. "Forecasting Financialand Economic Cycles provides a lively survey of the many ways that cyclical economic activity has been dissected and analyzed. With this book, an astute reader may even be able to anticipate the next cyclical turn." —Samuel D. Kahan, Chief Economist Fuji Securities, Inc. "The definitive book on the most important and enduringfeature of an often mist-bound economic landscape: the business cycle." —Alfred L. Malabre, Jr., Economics Editor, The Wall Street,Journal "Niemira and Klein cover both the theory of economic cyclesand methods for forecasting them. They provide one of the most comprehensive and current reviews of academic studies of economic cycles to be found anywhere." —Anthony F. Herbst, Professor of Finance, The University of Texas at El Paso "This book succeeds as a comprehensive, balanced, and accessible treatment of fluctuations in economic and financial activity. It should prove useful to all those in industry and finance who wish to understand and analyze the trends and changes in the modern dynamic economy." —Victor Zarnowitz, Professor Emeritus of Economics and Finance, University of Chicago

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

This text provides a thorough explanation of the non-financial economic indicators that are closely watched by the financial markets. It details how the indicators are compiled and what the statistical significance is for the economy, as well as presenting insights into interpreting the data.

This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.