02096cas a2200313 a 4500 AALejournalIMF022946 230101c9999 xx r poo 0 0eng d 20.00 USD 9798400211683 1018-5941 BD-DhAAL BD-DhAAL Ando, Sakai. Systematizing Macroframework Forecasting : High-Dimensional Conditional Forecasting with Accounting Identities / Sakai Ando, Taehoon Kim. Washington, D.C. : International Monetary Fund, 2022. 1 online resource (25 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Conditional Forecasting imf Forecasting and Other Model Applications imf Forecasting and Simulation imf General Aggregative Models imf Macroframework imf Kim, Taehoon.. IMF Working Papers; Working Paper ; No. 2022/110 Full text available on IMF https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=110 IMF e-Library