An econometric framework to nowcast low-frequency data

Qureshi, Irfan A., Ramayandi, Arief and Ahmad, Ghufran ORCID: https://orcid.org/0000-0002-2454-9335 2026. An econometric framework to nowcast low-frequency data. Journal of Forecasting Item availability restricted.

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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.

Item Type:	Article
Status:	In Press
Schools:	Schools > Business (Including Economics)
Subjects:	H Social Sciences > H Social Sciences (General)
Publisher:	Wiley
ISSN:	0277-6693
Date of First Compliant Deposit:	8 January 2026
Date of Acceptance:	30 December 2025
Last Modified:	08 Jan 2026 16:45
URI:	https://orca.cardiff.ac.uk/id/eprint/183459

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