Colombian Agricultural Sector’s Early Estimator of Gross Domestic Production Using Nowcasting and Big Data Methods

Authors

  • Diego Fernando Bravo Higueraa Universidad Nacional de Colombia , Colombia
  • León Darío Parra Bernal Universidad EAN, Colombia
  • Milenka Linneth Argote Cusi Women in Global Health , Colombia
  • Grace Andrea Torres Pineda Departamento Nacional de Estadística DANE

Keywords:

Nowcasting, Forecasting, google trends, Machine Learnig, Big Data

Abstract

Facing challenges like the COVID-19 pandemic, statistical production increasingly relies on non-traditional data sources for timely and accurate information. In this regard, The National Statistical Office of Colombia (DANE, by its acronym in Spanish) initiated a project, supported by the Statistics Advisory Council, to develop an early estimator for the Colombian agricultural sector. This paper presents the results for the implementation of a Ridge model and Zero Shot Classification to estimate the Gross Domestic Product (GDP) of the agricultural sector, leveraging Google News and Google Trends. Results reveal that these alternative sources offer valuable insights into economic trends. Combining machine learning techniques with Nowcasting methods yielded precise projections. The Ridge method demonstrated the lowest estimation error, providing an early GDP indicator for the agricultural sector of 8,188 billion Colombian pesos for 2022 Q2, 30 days ahead of official publication.

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Author Biographies

Diego Fernando Bravo Higueraa, Universidad Nacional de Colombia , Colombia

PhD student of Electrical Engineering at Universidad Nacional de Colombia-UNAL, where currently he works at Computer Imaging and Medical Applications Laboratory (CIM@LAB). In 2022 he works as Data Scientist at Departamento Administrativo Nacional de Estadística (DANE).

León Darío Parra Bernal , Universidad EAN, Colombia

Associate Professor and Director of Entrepreneurship Research Group at EAN University, and Technical Advisor of National Statistics System at Departamento Administrativo Nacional de Estadística (DANE).

Milenka Linneth Argote Cusi, Women in Global Health , Colombia

Ingeniera de Sistemas, Magister en Estudios de Población y Desarrollo por la Facultad Latinoamericana de Ciencias Sociales (FLACSO), Sede México, y Doctorante en Estadística de la Universidad Nacional de Colombia. Se ha desempeñado como asesora de la subdirección de monitoreo y evaluación de indicadores epidemiológicos en la secretaria de salud de México, dependencia CENSIDA, Y jefe de la plataforma de información y elaboración de indicadores de impacto en el Observatorio Urbano de la Ciudad de México en la FLACSO, México. Ha sido docente de las cátedras de análisis de procesos, cálculo, y desarrollo de sistemas georeferenciados en La Escuela Militar de Ingeniería en Bolivia. En la actualidad es docente del posgrado en Gestión del talento Humano de la Universidad Santo Tomás en Bogotá Colombia

Grace Andrea Torres Pineda, Departamento Nacional de Estadística DANE

Professor and researcher at Universidad Nacional de Colombia. In 2022 she works as Leader in Standarization Concepts and Methodologies in Departamento Administrativo Nacional de Estadística (DANE)

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Published

2024-07-11

How to Cite

Bravo Higueraa, D. F., Parra Bernal , L. D., Argote Cusi, M. L., & Torres Pineda, G. A. (2024). Colombian Agricultural Sector’s Early Estimator of Gross Domestic Production Using Nowcasting and Big Data Methods. Journal of Technology Management & Innovation, 19(2), 54–66. Retrieved from https://www.jotmi.org/index.php/GT/article/view/4364

Issue

Vol. 19 No. 2 (2024)

Section

Research Articles

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