The pressure to produce explanations and forecasts and the economic dichotomies which insist on appearing, lead to a desire to deal with the description, analysis and forecast of the phenomenon of business cycles using economic indicators. This text provides an introduction to business cycles and their theoretical and historical basis. It also includes work on early indicator research and provides examples of business cycle indicators.
This volume presents the most complete collection available of the work of Victor Zarnowitz, a leader in the study of business cycles, growth, inflation, and forecasting.. With characteristic insight, Zarnowitz examines theories of the business cycle, including Keynesian and monetary theories and more recent rational expectation and real business cycle theories. He also measures trends and cycles in economic activity; evaluates the performance of leading indicators and their composite measures; surveys forecasting tools and performance of business and academic economists; discusses historical changes in the nature and sources of business cycles; and analyzes how successfully forecasting firms and economists predict such key economic variables as interest rates and inflation.
Developed fifty years ago by the National Bureau of Economic Research, the analytic methods of business cycles and economic indicators enable economists to forecast economic trends by examining the repetitive sequences that occur in business cycles. The methodology has proven to be an inexpensive and useful tool that is now used extensively throughout the world. In recent years, however, significant new developments have emerged in the field of business cycles and economic indicators. This volume contains twenty-two articles by international experts who are working with new and innovative approaches to indicator research. They cover advances in three broad areas of research: the use of new developments in economic theory and time-series analysis to rationalise existing systems of indicators; more appropriate methods to evaluate the forecasting records of leading indicators, particularly of turning point probability; and the development of new indicators.
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.
Short-term Forecasting for Empirical Economists seeks to close the gap between research and applied short-term forecasting. The authors review some of the key theoretical results and empirical findings in the recent literature on short-term forecasting, and translate these findings into economically meaningful techniques to facilitate their widespread application to compute short-term forecasts in economics, and to monitor the ongoing business cycle developments in real time.
Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses.
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.
Existing economic indicators and indexes assess economic activity but no single indicator measures the general macro-economic performance of a nation, state, or region in a methodologically simple and intuitive way. This paper proposes a simple, yet informative metric called the Economic Performance Index (EPI). The EPI represents a step toward clarity, by combining data on inflation, unemployment, government deficit, and GDP growth into a single indicator. In contrast to other indexes, the EPI does not use complicated mathematical procedures but was designed for simplicity, making it easier for professionals and laypeople alike to understand and apply to the economy. To maximize ease of understanding, we adopt a descriptive grading system. In addition to a Raw EPI that gives equal weights to its components, we construct a Weighted EPI and show that both indexes perform similarly for U.S. data. To demonstrate the validity of the EPI, we conduct a review of U.S. history from 1790 to 2012. We show that the EPI reflects the major events in U.S. history, including wars, periods of economic prosperity and booms, along with economic depressions, recessions, and even panics. Furthermore, the EPI not only captures official recessions over the past century but also allows for measuring and comparing their relative severity. Even though the EPI is simple by its construction, we show that its dynamics are similar to those of the Chicago Fed National Activity Index (CFNAI) and The Conference Board Coincident Economic Index® (CEI).
