Nowcasting Annual National Accounts with Quarterly Indicators: An Assessment of Widely Used Benchmarking Methods

March 18, 2016

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Format: Chicago

Marco Marini. Nowcasting Annual National Accounts with Quarterly Indicators: An Assessment of Widely Used Benchmarking Methods, (USA: International Monetary Fund, 2016) accessed December 3, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

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.

Subject: Exports, Imports, International trade, National accounts, Trade in goods

Keywords: Absolute error, Benchmarking, BI ratio, Cholette-Dagum method, Chow-Lin method, Chow-Lin projection, Exports, Extrapolation, Imports, Quarterly National Accounts, Regression model, Trade in goods, WP

Publication Details

Pages:

25
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2016/071
Stock No:

WPIEA2016071
ISBN:

9781484301180
ISSN:

1018-5941

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