An Economic Framework to Nowcast Low-Frequency Data

Irfan Qureshi, Arief Ramayandi and Ghufran Ahmad
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Irfan Qureshi: Asian Development Bank
Arief Ramayandi: Asian Development Bank Institute
Ghufran Ahmad: Cardiff University

No 800, ADB Economics Working Paper Series from Asian Development Bank

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

Keywords: monitoring real activity; Kalman filter; dynamic factor model; annual nowcasting (search for similar items in EconPapers)
JEL-codes: C38 C53 E37 O11 O47 (search for similar items in EconPapers)
Pages: 36
Date: 2025-09-16
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Persistent link: https://EconPapers.repec.org/RePEc:ris:adbewp:021542

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