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An Econometric Framework to Nowcast Low‐Frequency Data

Author

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  • Irfan A. Qureshi
  • Arief Ramayandi
  • Ghufran Ahmad

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.

Suggested Citation

  • Irfan A. Qureshi & Arief Ramayandi & Ghufran Ahmad, 2026. "An Econometric Framework to Nowcast Low‐Frequency Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(4), pages 1473-1494, July.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:4:p:1473-1494
    DOI: 10.1002/for.70102
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