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Nowcasting del PIB argentino a través de un modelo de corrección de errores flexible
[Nowcasting Argentine's GDP through a flexible error correction model]

Author

Listed:
  • Frank, Luis

Abstract

The article proposes a nowcasting model to estimate Argentina's seasonally adjusted Monthly Estimator of Economic Activity (EMAE) using a reduced set of high-frequency economic variables (tax revenue, Portland cement dispatches, automobile sales, and electricity demand), available with a 5–7-day lag, covering data from January 2015 to June 2025. A traditional error correction model (ECM) is compared with a flexible version (FECM) that incorporates time-varying coefficients. The FECM, with $\lambda=1$, outperforms the ECM in accuracy (MAPE of 0.35 versus 1.04). Electricity demand and cement production are the most relevant indicators, while tax revenue has a lower impact. However, it is recommended to retain all variables, as their contribution depends on their joint inclusion. Additionally, a hybrid model that recursively updates parameters is proposed, offering an efficient alternative for real-time economic monitoring.

Suggested Citation

  • Frank, Luis, 2025. "Nowcasting del PIB argentino a través de un modelo de corrección de errores flexible [Nowcasting Argentine's GDP through a flexible error correction model]," MPRA Paper 126543, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:126543
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    File URL: https://mpra.ub.uni-muenchen.de/126543/1/MPRA_paper_126543.pdf
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    References listed on IDEAS

    as
    1. Kalaba, Robert E. & Tesfatsion, Leigh S., 1989. "Time-Varying Linear Regression Via Flexible Least Squares," Staff General Research Papers Archive 11196, Iowa State University, Department of Economics.
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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