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Ask, Think, Predict: LLM-Based Nowcasting of Argentina’s GDP

Author

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  • Gómez García Facundo Gonzalo
  • Manzano Quiroga Jeremías Ángel
  • Bernasconi María Sol

Abstract

We assess the forecasting performance of Gemini 2.0 Flash Thinking Experimental with Apps for Argentina’s seasonally adjusted quarter-over-quarter real GDP growth over 2021–2024. Using a transparent prompt-engineering protocol, we elicit point forecasts at three horizons and evaluate them in real time against the Central Bank’s Relevamiento de Expectativas de Mercado (REM). Across data vintages, Gemini delivers accuracy comparable to expert consensus—especially at nowcast and one-quarter-ahead horizons—while operating at effectively zero marginal cost. We also document where performance deteriorates (regime shifts and data revisions) and show that simple prompt safeguards improve stability. Overall, general-purpose LLMs can complement conventional workflows by providing competitive short-horizon forecasts with minimal implementation overhead.

Suggested Citation

  • Gómez García Facundo Gonzalo & Manzano Quiroga Jeremías Ángel & Bernasconi María Sol, 2025. "Ask, Think, Predict: LLM-Based Nowcasting of Argentina’s GDP," Asociación Argentina de Economía Política: Working Papers 4806, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4806
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    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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