Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities

June 3, 2022

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Format: Chicago

Sakai Ando, and Taehoon Kim. Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities, (USA: International Monetary Fund, 2022) accessed September 18, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

Forecasting a macroframework, which consists of many macroeconomic variables and accounting identities, is widely conducted in the policy arena to present an economic narrative and check its consistency. Such forecasting, however, is challenging because forecasters should extend limited information to the entire macroframework in an internally consistent manner. This paper proposes a method to systematically forecast macroframework by integrating (1) conditional forecasting with machine-learning techniques and (2) forecast reconciliation of hierarchical time series. We apply our method to an advanced economy and a tourism-dependent economy using France and Seychelles and show that it can improve the WEO forecast.

Subject: Balance of payments, Current account balance, GDP measurement, National accounts

Keywords: Accounting Identities, Accounting identity, Conditional Forecasting, Current account balance, Forecasting method, Framework forecasting, GDP measurement, Hierarchical Time Series, IMF working paper 22/110, Macroframework, Reconciliation, Unknown variable

Publication Details

Pages:

25
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/110
Stock No:

WPIEA2022110
ISBN:

9798400211683
ISSN:

1018-5941

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