IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1225.html
   My bibliography  Save this paper

El ISAE: Un Indicador para Monitorear la Actividad Económica Colombiana en Alta Frecuencia

Author

Listed:
  • Juan Pablo Cote-Barón
  • Karen L. Pulido-Mahecha
  • Nicol Valeria Rodríguez-Rodríguez
  • Carlos D. Rojas-Martínez

Abstract

El desarrollo de metodologías que permitan el diagnóstico del estado y la tendencia de la actividad económica es de especial importancia para la toma de decisiones de política económica. En este documento se propone un indicador semanal de actividad económica para Colombia, para el período comprendido entre febrero de 2000 y mayo de 2022. El indicador es obtenido como resultado de un modelo de factores dinámicos con un esquema de frecuencias mixtas, que emplea 32 variables semanales (10), mensuales (19) y trimestrales (3). Los resultados muestran que el indicador captura de forma adecuada los ciclos sobresalientes en el período de análisis, dentro de los cuales se destaca la reciente crisis originada por la pandemia del Covid-19. Además, sugieren que, como se espera, la capacidad del indicador para estimar el desempeño de la actividad económica en el trimestre mejora a medida que se cuenta con más información disponible, considerando los rezagos de publicación de la misma. **** ABSTRACT: The development of methodologies that enable the diagnosis of the current state and trend of economic activity is particularly important to improve the decision-making process in economic policy. This paper proposes a new weekly indicator of economic activity for Colombia, covering the period between February 2000 and May 2022. This indicator is the result of a mixed-frequency dynamic factor model that uses 32 weekly (10), monthly (19) and quarterly (3) variables. Our results suggest that the indicator adequately captures the main economic cycles in the period of analysis, prominent among which is the recent crisis generated by the Covid-19 pandemic. We also find that, given the lags in publication of data, the ability of the indicator to diagnose the state of economic activity improves as more information is available.

Suggested Citation

  • Juan Pablo Cote-Barón & Karen L. Pulido-Mahecha & Nicol Valeria Rodríguez-Rodríguez & Carlos D. Rojas-Martínez, 2023. "El ISAE: Un Indicador para Monitorear la Actividad Económica Colombiana en Alta Frecuencia," Borradores de Economia 1225, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1225
    DOI: 10.32468/be.1225
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.1225
    Download Restriction: no

    File URL: https://libkey.io/10.32468/be.1225?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2011. "EUROMIND: a monthly indicator of the euro area economic conditions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 439-470, April.
    3. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    4. Herman Kamil & José David Pulido & José Luis Torres, 2010. "El IMACO": un índice mensual líder de la actividad económica en Colombia"," Borradores de Economia 7129, Banco de la Republica.
    5. James H. Stock & Mark W. Watson, 2017. "Twenty Years of Time Series Econometrics in Ten Pictures," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 59-86, Spring.
    6. Deicy J. Cristiano & Manuel D. Hernández & José David Pulido, 2012. "Pronósticos de corto plazo en tiempo real para la actividad económica colombiana," Borradores de Economia 9827, Banco de la Republica.
    7. Gerhard Fenz & Helmut Stix, 2021. "Monitoring the economy in real time with the weekly OeNB GDP indicator: background, experience and outlook," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20-Q1/, pages 17-40.
    8. Diaz, Elena Maria & Perez-Quiros, Gabriel, 2021. "GEA tracker: A daily indicator of global economic activity," Journal of International Money and Finance, Elsevier, vol. 115(C).
    9. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    10. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    11. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    2. Christiane Baumeister & Danilo Leiva-León & Eric Sims, 2024. "Tracking Weekly State-Level Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 483-504, March.
    3. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    4. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    6. Mahmood, Asif & Masood, Hina, 2024. "A High-frequency Monthly Measure of Real Economic Activity in Pakistan," MPRA Paper 121838, University Library of Munich, Germany.
    7. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    8. Mantas Lukauskas & Vaida Pilinkienė & Jurgita Bruneckienė & Alina Stundžienė & Andrius Grybauskas & Tomas Ruzgas, 2022. "Economic Activity Forecasting Based on the Sentiment Analysis of News," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    9. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    10. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    11. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," American Economic Review, American Economic Association, vol. 100(2), pages 20-24, May.
    12. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
    13. Marcos Dal Bianco & Jaime Martinez-Martín & Maximo Camacho, 2013. "Short-Run Forecasting of Argentine GDP Growth," Working Papers 1314, BBVA Bank, Economic Research Department.
    14. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
    15. Martyna Marczak & Víctor Gómez, 2017. "Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter," Empirical Economics, Springer, vol. 52(4), pages 1379-1408, June.
    16. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    17. Eraslan, Sercan & Götz, Thomas, 2021. "An unconventional weekly economic activity index for Germany," Economics Letters, Elsevier, vol. 204(C).
    18. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    19. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    20. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.

    More about this item

    Keywords

    actividad económica; indicador semanal; modelo de factores dinámicos de frecuencias mixtas; economic activity; weekly indicator; mixed-frequency dynamic factor model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdr:borrec:1225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.