Effectiveness of Sentiment Analysis in Forecasting Ecuador’s GDP Annual Growth Rate Using Newspaper Textual Data (2001-2024)

Resumen

Ecuador’s ongoing crises underscore the need for timely economic forecasting. While business and consumer surveys capture expectations, their monthly frequency limits responsiveness to sudden shocks. This study tests whether sentiment analysis of daily newspaper articles can improve GDP growth forecasts. Through 240.000 articles from 2001- 2024, five sentiment indicators were constructed with a customized NLP approach combining TextBlob, VADER, and SpaCy’s spanish model. These indicators were incorporated into a LASSO-ARDL model alongside traditional controls and survey-based indicators. Results show that sentiment-enhanced models significantly outperform survey-based benchmark for short-term horizons (1-3 months), reducing RMSE and demonstrating greater stability. At longer horizons, differences become statistically insignificant. The findings highlight the value of integrating real-time, text-derived indicators into forecasting frameworks, offering a scalable complement to traditional methods in emerging and volatile economic contexts.