This paper discusses that for countries that have introduced new currencies, the rates shown in International Financial Statistics (IFS) for the period before the introduction of the most recent currency may be used as conversion factors—they may be used to convert national currency in IFS to US dollar or SDR. In such cases, the factors are constructed by chain linking the exchange rates of the old and the new currencies. The basis used is the value of the new currency relative to the old currency, as established by the issuing agency at the time the new currency was introduced. Notes on the introduction of new currencies can be found in the Country Notes or in IFS print publication (if recent). Data on members’ IMF accounts are presented in the Fund Position section in the country tables and in four world tables. Terms and concepts of IMF accounts and the time series in the country and world tables are explained below.

From the New York Times bestselling author of Big Data, a prediction for how data will revolutionize the market economy and make cash, banks, and big companies obsolete In modern history, the story of capitalism has been a story of firms and financiers. That's all going to change thanks to the Big Data revolution. As Viktor Mayer-Schörger, bestselling author of Big Data, and Thomas Ramge, who writes for The Economist, show, data is replacing money as the driver of market behavior. Big finance and big companies will be replaced by small groups and individual actors who make markets instead of making things: think Uber instead of Ford, or Airbnb instead of Hyatt. This is the dawn of the era of data capitalism. Will it be an age of prosperity or of calamity? This book provides the indispensable roadmap for securing a better future.

The audited consolidated financial statements of the International Monetary Fund as of April 30, 2019 and 2018

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 Manual, which updates the first edition published in 1986, is a major advance in the standards for compilation and presentation of fiscal statistics. It is intended as a reference volume for compilers of government finance statistics, fiscal analysts, and other users of fiscal data. The Manual introduces accrual accounting, balance sheets, and complete coverage of government economic and financial activities. It covers concepts, definitions, classifications, and accounting rules, and provides a comprehensive framework for analysis, planning, and policy determination. To the extent possible, the Manual has been harmonized with the System of National Accounts 1993.

Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

The October 2017 Global Financial Stability Report finds that the global financial system continues to strengthen in response to extraordinary policy support, regulatory enhancements, and the cyclical upturn in growth. It also includes a chapter that examines the short- and medium-term implications for economic growth and financial stability of the past decades’ rise in household debt. It documents large differences in household debt-to-GDP ratios across countries but a common increasing trajectory that was moderated but not reversed by the global financial crisis. Another chapter develops a new macroeconomic measure of financial stability by linking financial conditions to the probability distribution of future GDP growth and applies it to a set of 20 major advanced and emerging market economies. The chapter shows that changes in financial conditions shift the whole distribution of future GDP growth.