The purpose of the Review is to promote the collection, criticism, and interpretation of economic statistics, with a view to making them more accurate and valuable than they are at present for business and scientific purposes.

This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.

From Nobel Prize–winning economist and New York Times bestselling author Robert Shiller, a groundbreaking account of how stories help drive economic events—and why financial panics can spread like epidemic viruses Stories people tell—about financial confidence or panic, housing booms, or Bitcoin—can go viral and powerfully affect economies, but such narratives have traditionally been ignored in economics and finance because they seem anecdotal and unscientific. In this groundbreaking book, Robert Shiller explains why we ignore these stories at our peril—and how we can begin to take them seriously. Using a rich array of examples and data, Shiller argues that studying popular stories that influence individual and collective economic behavior—what he calls "narrative economics"—may vastly improve our ability to predict, prepare for, and lessen the damage of financial crises and other major economic events. The result is nothing less than a new way to think about the economy, economic change, and economics. In a new preface, Shiller reflects on some of the challenges facing narrative economics, discusses the connection between disease epidemics and economic epidemics, and suggests why epidemiology may hold lessons for fighting economic contagions.

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.

This original and panoramic book proposes that the underlying forces of demography and globalisation will shortly reverse three multi-decade global trends – it will raise inflation and interest rates, but lead to a pullback in inequality. “Whatever the future holds”, the authors argue, “it will be nothing like the past”. Deflationary headwinds over the last three decades have been primarily due to an enormous surge in the world’s available labour supply, owing to very favourable demographic trends and the entry of China and Eastern Europe into the world’s trading system. This book demonstrates how these demographic trends are on the point of reversing sharply, coinciding with a retreat from globalisation. The result? Ageing can be expected to raise inflation and interest rates, bringing a slew of problems for an over-indebted world economy, but is also anticipated to increase the share of labour, so that inequality falls. Covering many social and political factors, as well as those that are more purely macroeconomic, the authors address topics including ageing, dementia, inequality, populism, retirement and debt finance, among others. This book will be of interest and understandable to anyone with an interest on where the world’s economy may be going.