Addressing the role that the transportation sector plays in business cycle propagation, this title presents an index of the different indicators for this sector to identify its state, and predict its future, using various statistical procedures.

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.

This paper analyzes the evolution of the degree of global cyclical interdependence over the period 1960-2005. We categorize the 106 countries in our sample into three groups-industrial countries, emerging markets, and other developing economies. Using a dynamic factor model, we then decompose macroeconomic fluctuations in key macroeconomic aggregates-output, consumption, and investment-into different factors. These are: (i) a global factor, which picks up fluctuations that are common across all variables and countries; (ii) three group-specific factors, which capture fluctuations that are common to all variables and all countries within each group of countries; (iii) country factors, which are common across all aggregates in a given country; and (iv) idiosyncratic factors specific to each time series. Our main result is that, during the period of globalization (1985-2005), there has been some convergence of business cycle fluctuations among the group of industrial economies and among the group of emerging market economies. Surprisingly, there has been a concomitant decline in the relative importance of the global factor. In other words, there is evidence of business cycle convergence within each of these two groups of countries but divergence (or decoupling) between them.

Traditionally, economic growth and business cycles have been treated independently. However, the dependence of GDP levels on its history of shocks, what economists refer to as “hysteresis,” argues for unifying the analysis of growth and cycles. In this paper, we review the recent empirical and theoretical literature that motivate this paradigm shift. The renewed interest in hysteresis has been sparked by the persistence of the Global Financial Crisis and fears of a slow recovery from the Covid-19 crisis. The findings of the recent literature have far-reaching conceptual and policy implications. In recessions, monetary and fiscal policies need to be more active to avoid the permanent scars of a downturn. And in good times, running a high-pressure economy could have permanent positive effects.

In recent decades the American economy has experienced the worst peace-time inflation in its history and the highest unemployment rate since the Great Depression. These circumstances have prompted renewed interest in the concept of business cycles, which Joseph Schumpeter suggested are "like the beat of the heart, of the essence of the organism that displays them." In The American Business Cycle, some of the most prominent macroeconomics in the United States focuses on the questions, To what extent are business cycles propelled by external shocks? How have post-1946 cycles differed from earlier cycles? And, what are the major factors that contribute to business cycles? They extend their investigation in some areas as far back as 1875 to afford a deeper understanding of both economic history and the most recent economic fluctuations. Seven papers address specific aspects of economic activity: consumption, investment, inventory change, fiscal policy, monetary behavior, open economy, and the labor market. Five papers focus on aggregate economic activity. In a number of cases, the papers present findings that challenge widely accepted models and assumptions. In addition to its substantive findings, The American Business Cycle includes an appendix containing both the first published history of the NBER business-cycle dating chronology and many previously unpublished historical data series.

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.

Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.