Copyright Page
© 2022 International Monetary Fund WP/22/110
IMF Working Paper
Research Department
Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities
Prepared by Sakai Ando and Taehoon Kim*
Authorized for distribution by Prachi Mishra
June 2022
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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.
RECOMMENDED CITATION: Ando, Sakai and Taehoon Kim (2022), “Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities,” IMF Working Paper 22/110
| JEL Classification Numbers: | C53, E17, E27 |
| Keywords: | Macroframework, Conditional Forecasting, Reconciliation, Accounting Identities, Hierarchical Time Series |
| Author’s E-Mail Address: | sando@imf.org, tkim@imf.org |
| JEL Classification Numbers: | C53, E17, E27 |
| Keywords: | Macroframework, Conditional Forecasting, Reconciliation, Accounting Identities, Hierarchical Time Series |
| Author’s E-Mail Address: | sando@imf.org, tkim@imf.org |
Title Page
WORKING PAPERS
Systematizing macroframework forecasting
High-Dimensional Conditional Forecasting with Accounting Identities
Prepared by Sakai Ando and Taehoon Kim
Contents
1. Introduction
2. General Framework
2.1 Step 1: Forecasting Each Unknown Variable
2.2 Step 2: Forecast Reconciliation
3. Country Example
3.1 Data
3.2 Accounting Identities
3.3 Variables to Forecast
3.4 Step 1: Forecasting Each Unknown Variable
3.5 Step 2: Forecast Reconciliation
3.6 Performance Assessment
4. Discussion
5. Conclusion
Annex 1. Proof of Theorem 1
Annex 2. Another Country Example: Seychelles
Annex 3. Forecast Error of Unknown Variables
References
FIGURES
Figure 1. Mean RMSE of Four Forecasting Methods
Figure 2. RMSE of Unknown Variables
Figure 3. Mean RMSE of Four Forecasting Methods (Left) and RMSE of Each Unknown Variable (Right)
TABLES
Table 1. Data Structure
Table 2. Time Series Split
Table 3. List of WEO Variables
Table 4. List of WEO Variables