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

DOI:

https://doi.org/10.4067/S0718-27242024000200054

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)

References

Argote, M., & Parra, L. (2020). Global Entrepreneurship Analytics, using GEM Data. Routledge, Taylor and Francis. https://doi.org/10.4324/9780429316715

Ashouri, M., Cai, K., Lin, F., & Shmueli, G. (2018). Assessing the value of an information system for developing predictive analytics: The case of forecasting school-level demand in Taiwan. Service Science, 10(1), 58–75. https://doi.org/10.1287/serv.2017.0200

Banbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting. In L. Oxford Academy (Ed.), Oxford Handbook on Economic Forecasting (pp. 193-224). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195398649.013.0008

Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133–160. https://doi.org/10.1002/jae.2306

Big Data-Statista. (2022). Amount of data created, consumed, and stored 2010-2023, with forecasts to 2025. Global state of big data/AI adoption 2023. Statista.

Bok, B., Caratelli, D., Giannone, D., Sbordone, A. M., & Tambalotti, A. (2018). Macroeconomic nowcasting and forecasting with big data. Annual Review of Economics, 10, 615–643. https://doi.org/10.1146/annurev-economics-080217-053214

CCSA. (2020). How COVID-19 is changing the world: A statistical perspective, Volume II. ReliefWeb. Retrieved from https://www.reliefweb.int

Cepni, O., Güney, I. E., & Swanson, N. R. (2019). Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes. International Journal of Forecasting, 35(2), 555–572. https://doi.org/10.1016/j.ijforecast.2018.10.008

Chakraborty, S., Mengersen, K., Fidge, C., Ma, L., & Lassen, D. (2016). A Bayesian Network-based customer satisfaction model: A tool for management decisions in railway transport. Decision Analytics, 3(1), 1–24. https://doi.org/10.1186/s40165-016-0021-2

Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x

Corona, F., González-Farías, G., & López-Pérez, J. (2021). A nowcasting approach to generate timely estimates of Mexican economic activity: An application to the period of COVID-19. ArXiv Preprint ArXiv:2101.10383. https://doi.org/10.48550/arXiv.2101.10383

DANE. (2020). Boletín Técnico Producto Interno Bruto (PIB). Retrieved from https://www.dane.gov.co/files/investigaciones/boletines/pib/bol_PIB_Itrim20_producion_y_gasto.pdf

Dauphin, M. J.-F., Dybczak, M. K., Maneely, M., Sanjani, M. T., Suphaphiphat, M. N., Wang, Y., & Zhang, H. (2022). Nowcasting GDP-A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies. International Monetary Fund.

Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). Forecasting Indonesia exports using a hybrid model ARIMA-LSTM. Procedia Computer Science, 179, 480–487. https://doi.org/10.1016/j.procs.2021.01.031

DSCN. (2020). Primera estimación rápida del Indicador de Seguimiento a la Economía. Ed. Departamento Nacional de Estadística, DANE, Bogotá, Colombia.

(Eurostat), E. C. (2016). Overview of GDP flash estimation methods: 2016 edition. Publications Office. https://doi.org/10.2785/51658

(Eurostat), E. C. (2020). Methodological note GUIDANCE ON QUARTERLY NATIONAL ACCOUNTS ( INCLUDING FLASH ) ESTIMATES IN THE CONTEXT OF THE COVID-19 CRISIS.

Feldmeyer, D., Nowak, W., Jamshed, A., & Birkmann, J. (2021). An open resilience index: Crowdsourced indicators empirically developed from natural hazard and climatic event data. Science of the Total Environment, 774, 145-734. https://doi.org/10.1016/j.scitotenv.2021.145734

Giannone, D., Lenza, M., & Primiceri, G. E. (2021). Economic predictions with big data: The illusion of sparsity. Econometrica, 89(5), 2409–2437. https://doi.org/10.3982/ECTA17842

Guevara, D. (2021). Crisis del COVID-19: Impactos socioeconómicos y retos para países Latinoamericanos. Cuadernos de Economía, 40(85), 1-6.

Jardet, C., & Meunier, B. (2022). Nowcasting world GDP growth with high-frequency data. Journal of Forecasting, 41(6), 1181-1200. https://doi.org/10.1002/for.2858

Joseph, A., Kalamara, E., Kapetanios, G., & Potjagailo, G. (2021). WITHDRAWN: Forecasting UK inflation bottom up. International Journal of Forecasting, 3(5), 1-15. https://doi.org/10.1016/j.ijforecast.2021.03.005

Levenberg, A., Simpson, E., Roberts, S., & Gottlob, G. (2013). Economic Prediction using heterogeneous data streams from the World Wide Web. In Scalable Decision Making: Uncertainty, Imperfection, Deliberation (SCALE), Proceedings of ECML/PKDD Workshop.

Lewis, N. D. (2016). Deep Time Series Forecasting with Python: An Intuitive Introduction to Deep Learning for Applied Time Series Modeling. Create Space Independent Publishing Platform.

Li, J., Li, G., Liu, M., Zhu, X., & Wei, L. (2022). A novel text-based framework for forecasting agricultural futures using massive online news headlines. International Journal of Forecasting, 38(1), 35–50. https://doi.org/10.1016/j.ijforecast.2020.02.002

Mariano, R. S., & Murasawa, Y. (2010). A coincident index, common factors, and monthly real GDP. Oxford Bulletin of Economics and Statistics, 72(1), 27–46. https://doi.org/10.1111/j.1468-0084.2009.00567.x

Marquardt, D. W., & Snee, R. D. (1975). Ridge regression in practice. The American Statistician, 29(1), 3–20. https://doi.org/10.2307/2683673

Martínez, W., Nieto, F. H., & Poncela, P. (2016). Choosing a dynamic common factor as a coincident index. Statistics & Probability Letters, 109, 89–98. https://doi.org/10.1016/j.spl.2015.11.008

Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2021). Machine learning advances for time series forecasting. Journal of Economic Surveys, 1(37), 76-111. https://doi.org/10.1111/joes.12429

Menculini, L., Marini, A., Proietti, M., Garinei, A., Bozza, A., Moretti, C., & Marconi, M. (2021). Comparing prophet and deep learning to ARIMA in forecasting wholesale food prices. Forecasting, 3(3), 644–662. https://doi.org/10.3390/forecast3030040

Richardson, A., van Florenstein Mulder, T., & Vehbi, T. (2021). Nowcasting GDP using machine-learning algorithms: A real-time assessment. International Journal of Forecasting, 37(2), 941–948. https://doi.org/10.1016/j.ijforecast.2020.10.005

Tuo, S., Chen, T., He, H., Feng, Z., Zhu, Y., Liu, F., & Li, C. (2021). A regional industrial economic forecasting model based on a deep convolutional neural network and big data. Sustainability, 13(22), 12789. https://doi.org/10.3390/su132212789

Wang, C., & Cao, Y. (2021). Forecasting Chinese economic growth, energy consumption, and urbanization using two novel grey multivariable forecasting models. Journal of Cleaner Production, 299, 126863. https://doi.org/10.1016/j.jclepro.2021.126863

Xie, G., Qian, Y., & Wang, S. (2021). Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tourism Management, 82, 104208. https://doi.org/10.1016/j.tourman.2020.104208

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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. https://doi.org/10.4067/S0718-27242024000200054

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Research Articles

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