Statistical offices have often recourse to benchmarking methods for compiling quarterly national accounts (QNA). Benchmarking methods employ quarterly indicator series (i) to distribute annual, more reliable series of national accounts and (ii) to extrapolate the most recent quarters not yet covered by annual benchmarks. The Proportional First Differences (PFD) benchmarking method proposed by Denton (1971) is a widely used solution for distribution, but in extrapolation it may suffer when the movements in the indicator series do not match consistently the movements in the target annual benchmarks. For this reason, an enhanced formula for extrapolation was recommended by the IMF’s Quarterly National Accounts Manual: Concepts, Data Sources, and Compilation (2001). We discuss the rationale behind this technique, and propose a matrix formulation of it. In addition, we present applications of the enhanced formula to artificial and real-life benchmarking examples showing how the extrapolations for the most recent quarters can be improved.

This Manual provides guidance to compilers of national accounts on the concepts, data sources, and compilation methods required for development of a system of quarterly national accounts. More and more countries are recognizing that quarterly national accounts are an essential tool for management and analysis of their economy. The Manual is intended particularly for compilers who already have a knowledge of annual national accounting concepts and methods, and provides techniques for the development of a consistent time series of annual and quarterly accounts. It serves as acomplement to the System of National Accounts 1993, which has only a limited discussion of quarterly accounts, and will also prove useful as a tool for sophisticated users of quarterly national accounts.

Benchmarking methods can be used to extrapolate (or “nowcast”) low-frequency benchmarks on the basis of available high-frequency indicators. Quarterly national accounts are a typical example, where a number of monthly and quarterly indicators of economic activity are used to calculate preliminary annual estimates of GDP. Using both simulated and real-life national accounts data, this paper aims at assessing the prediction accuracy of three benchmarking methods widely used in the national accounts compilation: the proportional Denton method, the proportional Cholette-Dagum method with first-order autoregressive error, and the regression-based Chow-Lin method. The results show that the Cholette-Dagum method provides the most accurate extrapolations when the indicator and the annual benchmarks move along the same trend. However, the Denton and Chow-Lin methods could prevail in real-life cases when the quarterly indicator temporarily deviates from the target series.

Time series play a crucial role in modern economies at all levels of activity and are used by decision makers to plan for a better future. Before publication time series are subject to statistical adjustments and this is the first statistical book to systematically deal with the methods most often applied for such adjustments. Regression-based models are emphasized because of their clarity, ease of application, and superior results. Each topic is illustrated with real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed a real data example is followed throughout the book.

The Quarterly National Accounts Manual (the Manual) provides conceptual and practical guidance for compiling quarterly national accounts (QNA) statistics. The Manual offers a comprehensive review of data sources, statistical methods, and compilation techniques to derive official estimates of quarterly GDP. The new edition—which upgrades the first edition, published in 2001—improves and expands the previous content based on recent methodological advances, best country practices, and suggestions received from QNA compilers and experts.

The Report on the Observance of Standards and Codes data module provides an assessment of Japan’s macroeconomic statistics against the Special Data Dissemination Standard, complemented by an assessment of data quality based on the IMF’s Data Quality Assessment Framework, July 2003 version. The assessment reveals that the data-producing agencies in Japan adhere to the principle of objectivity in the collection, processing, and dissemination of statistics. They demonstrate professionalism and are transparent in their policies and practices. Data are protected by strict measures of confidentiality throughout the data-producing agencies.

In March 2017, the IMF published an upgrade of its Direction of Trade Statistics (DOTS) dataset. This paper documents the new methodology that has been developed to estimate missing observations of bilateral trade statistics on a monthly basis. The new estimation procedure is founded on a benchmarking method that produces monthly estimates based on official trade statistics by partner country reported at different times and frequencies. In this paper we describe the new estimation methodology. Additional data sources have also been incorporated. We also assess the impact of the new estimates on trade measurement in DOTS at global, regional, and country-specific levels. Finally, we suggest some developments of DOTS to strenghten its relevance for IMF bilateral and multilateral surveillance.