Systematizing Macroframework Forecasting : High-Dimensional Conditional Forecasting with Accounting Identities /
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...
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| Formaat: | Tijdschrift |
| Taal: | English |
| Gepubliceerd in: |
Washington, D.C. :
International Monetary Fund,
2022.
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| Reeks: | IMF Working Papers; Working Paper ;
No. 2022/110 |
| Onderwerpen: | |
| Online toegang: | Full text available on IMF |
| Samenvatting: | 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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| Beschrijving item: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Fysieke beschrijving: | 1 online resource (25 pages) |
| Formaat: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Toegang: | Electronic access restricted to authorized BRAC University faculty, staff and students |