Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/336512 
Year of Publication: 
2025
Series/Report no.: 
ADB Economics Working Paper Series No. 800
Publisher: 
Asian Development Bank (ADB), Manila
Abstract: 
Standard nowcasting frameworks commonly use weekly or monthly variables to monitor quarterly gross domestic product (GDP). However, this method is not suitable for economies that track GDP annually. We modify the state-space representation of an otherwise standard dynamic factor model to represent annual variables as a linear combination of latent monthly indicators for more frequently released variables. Using data from a lower middle-income country, we derive a monthly activity measure that effectively tracks annual GDP growth. These estimates outperform institutional forecasts and competing approaches to estimate low-frequency data. The model offers broader applications to countries facing data limitations, especially lower-income countries.
Subjects: 
monitoring real activity
Kalman filter
dynamic factor model
annual nowcasting
JEL: 
C38
C53
E37
O11
O47
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Working Paper

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