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An Economic Framework to Nowcast Low-Frequency Data
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.
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.
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