GDP nowcasting with Machine Learning and Unstructured Data to Peru

Juan Tenorio and Wilder Pérez

No 197, Working Papers from Peruvian Economic Association

Abstract: In a context of ongoing change, “nowcasting” models based on Machine Learning (ML) algorithms deliver a noteworthy advantage for decision-making in both the public and private sectors due to its flexibility and ability to drive large amounts of data. This document presents projection models for the monthly GDP rate growth of Peru, which incorporate structured macroeconomic indicators with high-frequency unstructured sentiment variables. The window sampling comes from January 2007 to May 2023, including a total of 91 variables. By assessing six ML algorithms, the best predictors for each model were identified. The results reveal the high capacity of each ML model with unstructured data to provide more accurate and anticipated predictions than traditional time series models, where the outstanding models were Gradient Boosting Machine, LASSO, and Elastic Net, which achieved a prediction error reduction of 20% to 25% compared to the AR and Dynamic Factor Models (DFM) models. These results could be influenced by the analysis period, which includes crisis events featured by high uncertainty, where ML models with unstructured data improve significance.

Keywords: nowcasting; machine learning; GDP growth (search for similar items in EconPapers)
Date: 2023-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:apc:wpaper:197

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