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|a 1018-5941
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|a Ando, Sakai.
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|a Systematizing Macroframework Forecasting :
|b High-Dimensional Conditional Forecasting with Accounting Identities /
|c Sakai Ando, Taehoon Kim.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2022.
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|a 1 online resource (25 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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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|a Mode of access: Internet
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|a Conditional Forecasting
|2 imf
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|a Forecasting and Other Model Applications
|2 imf
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|a Forecasting and Simulation
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|a General Aggregative Models
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|a Macroframework
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|a Kim, Taehoon..
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|a IMF Working Papers; Working Paper ;
|v No. 2022/110
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|z Full text available on IMF
|u https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=110
|z IMF e-Library
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