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

This is the most sophisticated and up-to-date econometric analysis of business cycles now available. Francis Diebold and Glenn Rudebusch have long been acknowledged as leading experts on business cycles. And here they present a highly integrative collection of their most important essays on the subject, along with a detailed introduction that draws together the book's principal themes and findings. Diebold and Rudebusch use the latest quantitative methods to address five principal questions about the measurement, modeling, and forecasting of business cycles. They ask whether business cycles have become more moderate in the postwar period, concluding that recessions have, in fact, been shorter and shallower. They consider whether economic expansions and contractions tend to die of "old age." Contrary to popular wisdom, they find little evidence that expansions become more fragile the longer they last, although they do find that contractions are increasingly likely to end as they age. The authors discuss the defining characteristics of business cycles, focusing on how economic variables move together and on the timing of the slow alternation between expansions and contractions. They explore the difficulties of distinguishing between long-term trends in the economy and cyclical fluctuations. And they examine how business cycles can be forecast, looking in particular at how to predict turning points in cycles, rather than merely the level of future economic activity. They show here that the index of leading economic indicators is a poor predictor of future economic activity, and consider what we can learn from other indicators, such as financial variables. Throughout, the authors make use of a variety of advanced econometric techniques, including nonparametric analysis, fractional integration, and regime-switching models. Business Cycles is crucial reading for policymakers, bankers, and business executives.

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

As the 55th anniversary of the bank holiday of March 1933 approached, financial instability was a main topic in the financial press. Daily reports appeared of international debt crises, of the covert bankruptcy of deposit insurance, and of the near bankruptcy of one great financial institution after another. The great stock market crash of October 19 and 20, 1987, demonstrated that extreme instability can happen. It is generally asserted that the consequences of October 19th and 20th would have been disastrous if the Federal Reserve and Treasury interventions had not set things right. In 1933, financial markets in the United States and throughout the capitalist world collapsed. In the light of historical experience, the past 55 years are the anomaly. The papers collected in this volume come from various backgrounds and research paradigms. A common theme runs through these papers that makes the collection both interesting and important: The authors take seriously the obvious evidence that capitalist economies progress through time by lurching. Whether a particular study starts from household utility maximization or from the processes by which productive structures are reproduced and expanded, the authors are united in accepting the evidence that financial instability is a significant characteristic of modern capitalism.

The inability of forecasters to predict accurately the 1990-1991 recession emphasizes the need for better ways for charting the course of the economy. In this volume, leading economists examine forecasting techniques developed over the past ten years, compare their performance to traditional econometric models, and discuss new methods for forecasting and time series analysis.

Over the last decade, dynamical systems theory and related nonlinear methods have had a major impact on the analysis of time series data from complex systems. Recent developments in mathematical methods of state-space reconstruction, time-delay embedding, and surrogate data analysis, coupled with readily accessible and powerful computational facilities used in gathering and processing massive quantities of high-frequency data, have provided theorists and practitioners unparalleled opportunities for exploratory data analysis, modelling, forecasting, and control. Until now, research exploring the application of nonlinear dynamics and associated algorithms to the study of economies and markets as complex systems is sparse and fragmentary at best. Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters. Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.