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

Sakai Ando and Taehoon Kim

No 2022/110, IMF Working Papers from International Monetary Fund

Abstract: 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.

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 (search for similar items in EconPapers)
Pages: 25
Date: 2022-06-03
New Economics Papers: this item is included in nep-acc, nep-big and nep-for
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