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Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities
Author:
Mr. Sakai Ando
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https://orcid.org/0000-0003-2785-4375
and
Mr. Taehoon Kim
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Series:
IMF Working Papers
Volume/Issue:
Volume 2022: Issue 110
Language:
English
Publication Date:
03 Jun 2022
DOI:
https://doi.org/10.5089/9798400211683.001
ISBN:
9798400211683
ISSN:
1018-5941
Pages:
25
Keywords:
Macroframework; Conditional Forecasting; Reconciliation; Accounting Identities; Hierarchical Time Series; accounting identity; forecasting method; IMF working paper 22/110; framework forecasting; unknown variable; Current account balance; GDP measurement
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund