Detection of cycling points using a parametric method: Application of a Markov switching model for the Santa Fe economic cycle

Authors

DOI:

https://doi.org/10.24215/18521649e042

Keywords:

bussiness fluctuations, cycles, turning points, Markov Switching Model, regional economic activity

Abstract

This paper explores the use of Markov switching model in order to determinate the coincident composite index’s state changes of Santa Fe, Argentina, offering an alternative to traditional empirical methods. The results indicate that two-regimen model largely coincides with classic recessions and expansions identified previously, validating aggregation methodology robustness used to calculate coincident index and verifying the chronology which emerges from empirical approaches. Finally, usefulness of filtered probabilities to anticipate economic turning points is examined.

Author Biographies

  • Francisco Leiva, Bolsa de Comercio de Santa Fe, Argentina

    Francisco Leiva was born on December 6, 1995, in Santa Fe. He holds a Bachelor's degree in Economics from UNL (2021) and has been a full professor at UCU since 2022. He currently works at the Research Center of the Santa Fe Stock Exchange, where he specializes in economic outlook analysis and business cycles. Throughout his academic training, he has presented several papers at conferences and in journals. His academic background includes various training courses, notably a diploma in Computational Social Sciences (UNSAM, 2023) and a Master's degree in Applied Economics (UBA), which he has been pursuing since 2024.

     
  • Pedro Pablo Cohan, Bolsa de Comercio de Santa Fe, Argentina

    Pedro Pablo Cohan is an Argentine economist born on September 17, 1981, in Eckernförde, Germany. He completed his undergraduate studies at the Catholic University of Santa Fe in 2006 and later earned two master’s degrees: one in Economic Development in 2015 and another in Finance in 2019, both from the National University of Rosario. He is currently working on his doctoral thesis in Economics at the Pontifical Catholic University of Argentina. Professionally, he coordinates the Research Center of the Santa Fe Stock Exchange, as part of an interdisciplinary team focused on macroeconomic, regional, and market analysis.

  • Agustín Rodriguez, Bolsa de Comercio de Santa Fe, Argentina

    Agustín Rodriguez was born on July 2, 1999, in the town of Castelar, in the province of Santa Fe. He holds a Bachelor's degree in Economics from UNL (2023) and completed a postgraduate program in Data Science applied to business and public sector analysis at UNC (2024). Since 2022, he has been working at the Research and Services Center of the Santa Fe Stock Exchange, where he specializes in the analysis of the agricultural sector, with a particular focus on the central-northern region of the province of Santa Fe, as well as in the study of business cycles at both the national and provincial levels. In addition, he has been an adjunct professor in the Department of Economics at the Faculty of Economic Sciences of UNL since 2024.

References

Abad, A. M., Cristóbal, A. y Quilis, E. M. (2000). Fluctuaciones económicas, puntos de giro y clasificación cíclica. Instituto Nacional de Estadística.

Achuthan, L. y Banerji, A. (2004). Beating the business Cycle: How to predict and profit from turning points in the economy. Crown Business.

Agencia de Recaudación y Control Aduanero. (2023). Anexos estadísticos. https://www.afip.gob.ar/institucional/estudios/informe-de-recaudacion-anexos/2023.asp

Anas, J. y Ferrara, L. (2004). A comparative assessment of parametric and nonparametric turning points detection methods: the case of the Euro-zone economy. En G. L. Mazzi y G. Savio (Eds.), Monographs of official statistics. Papers and proceedings of the third Eurostat colloquium on modern tools for business cycle analysis (pp. 86-121). European communities.

Banco Central de la República Argentina. (2024). Tipos de cambio de Referencia Comunicación A 3500 (Mayorista) y Tipo de Cambio Nominal Promedio Mensual (TCNPM). BCRA. https://www.bcra.gob.ar/PublicacionesEstadisticas/Tipos_de_cambios.asp

Bello, O., Cantú, F. y Acevedo, A. (2010). Indicadores adelantados para América Latina. CEPAL. https://hdl.handle.net/11362/5335

Bry, G. y Boschan, C. (1971). Cyclical analysis of time series: selected procedures and computer programs. National Bureau of Economic Research. https://www.nber.org/books-and-chapters/cyclical-analysis-time-series-selected-procedures-and-computer-programs

Burns, A. F. y Mitchell, W. C. (1946). Measuring business cycles. National Bureau of Economic Research. https://www.nber.org/books-and-chapters/measuring-business-cycles

Camacho, M., Pacce, M. y Ulloa, C. (2017). Business cycle phases in Spain (Documento de Trabajo Nº17/20). BBVA Research. https://www.bbvaresearch.com/en/publicaciones/business-cycle-phases-in-spain/

Carpio Fragoso, R. I. (2020). Crédito bancario en el ciclo económico de México, 2003-2019: una aplicación del algoritmo Bry-Boschan y el filtro de Kalman [Tesis de maestría, Centro de Investigación y Docencia Económicas]. http://hdl.handle.net/11651/4293

Cartaya, V., Sáez, F. y Zavarce, H. (2010). Ciclos de actividad económica y comovimientos sectoriales (Serie documentos de trabajo 110). Banco Central de Venezuela.

Chaverri Morales, C. y Van Patten Rivera, D. (2012). Diseño de un indicador adelantado para la actividad económica de Costa Rica. Foro de Investigadores de Bancos Centrales del Consejo Monetario Centroamericano, (VII Foro de Investigadores: Santo Domingo, República Dominicana. 27 y 28 de Junio 2013).

de Jong, R. M. y Sakarya, N. (2016). The econometrics of the Hodrick-Prescott filter. Review of Economics and Statistics, 98(2), 310-317. https://doi.org/10.1162/REST_a_00523

D'Jorge, M. L. y Cohan, P. P. (2015). Índice compuesto coincidente de actividad económica para la provincia de Santa Fe (Argentina): indicador mensual de alcance sub-nacional. Centro de Estudios y Servicios de la Bolsa de Comercio de Santa Fe.

Eurostat, The Conference Board y UNO. (2017). Handbook on ciclical composite indicators for business cycle analysis – 2017 edition. European communities. https://doi.org/10.2785/962890

Filardo, A. J. (1994). Business-Cycle Phases and Their Transitional Dynamics. Journal of Business & Economic Statistics, 12(3), 299-308. https://doi.org/10.1080/07350015.1994.10524545

Gómez Aguirre, M. (2018). Determinación de los puntos de giro en la actividad a partir del índice sintético de actividad de la construcción. (1ª ed.). Cámara Argentina de la Construcción. https://biblioteca.camarco.org.ar/libro/determinacion-de-los-puntos-de-giro-en-la-actividad-a-partir-del-indice-sintetico-de-actividad-de-la-construccion/

Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2),357-384.

Hamilton, J. D. y Perez-Quiros, G. (1996). What do the Leading Indicators Lead? Journal of Business, 69(1), 27-49. https://doi.org/10.1086/209678

Hodrick, R. J. y Prescott, E. C. (1997). Postwar U.S. Business Cycles: An empirical Investigation. Journal of Money, Credit, and Banking, 29(1), 1-16. https://doi.org/10.2307/2953682

Instituto Nacional de Estadística y Censos. (2023). Censo 2022. Síntesis de resultados. INDEC. https://www.indec.gob.ar/indec/web/Nivel4-Tema-2-41-165

Instituto Nacional de Estadística y Censos. (2023). Mercado de trabajo. Tasas e indicadores socioeconómicos. Cuarto trimestre de 2022. Buenos Aires. INDEC. https://www.indec.gob.ar/indec/web/Institucional-Indec-InformesTecnicos-58

Instituto Nacional de Estadística y Censos. (2024). Agregados macroeconómicos: Diciembre de 2023, Informe de avance del nivel de actividad. INDEC. https://www.indec.gob.ar/indec/web/Nivel4-Tema-3-9-47

Instituto Nacional de Estadística y Censos. (2024). Intercambio Comercial Argentino: Diciembre de 2023. INDEC. https://www.indec.gob.ar/indec/web/Nivel4-Tema-3-2-40

Instituto Provincial de Estadística y Censos. (2024). Producto Bruto Geográfico. Informe de 2004-2023. IPEC. https://www.estadisticasantafe.gob.ar/contenido/informes-producto-bruto-geografico/

Johnson, C. A. (2001). Working Papers N° 84: Un Modelo de Switching para el Crecimiento en Chile. Banco Central de Chile. https://www.bcentral.cl/en/detail-news-and-publications/-/asset_publisher/Exzd7l9NC3Y6/content/documento-de-trabajo-n-84

Jorrat, J. (2005) Construcción de índices compuestos mensuales coincidente y líder de Argentina. En M. Marchionni (Ed.), Avances en Econometría (pp. 43-100). Temas Grupo Editorial.

Kydland, F. E. y Prescott, E. C. (1982). Time to Build and Aggregate Fluctuations. Econometrica: Journal of the Econometric Society, 50(6), 1345-1370. https://doi.org/10.2307/1913386

Lago, F. (2019). Análisis de los puntos de giro en la actividad de la construcción. FODECO. https://biblioteca.camarco.org.ar/libro/analisis-de-los-puntos-de-giro-en-la-actividad-de-la-construccion/

Lucas, R. E. (1972). Expectations and the neutrality of money. Journal of Economic Theory, 4(2), 103-124. https://doi.org/10.1016/0022-0531(72)90142-1

Mazzi, G. y Calès, L. (2017). An overview of alternative turning points composite indicators. En Eurostat, The Conference Board y UNO (Eds.), Handbook on ciclical composite indicators for business cycle analysis: 2017 edition (pp. 315-348). European Union and the United Nations.

Mintz, I. (1974). Dating united states growth cycles. En National Bureau of Economic Research (Ed.), Explorations in Economic Research (Vol. 1, pp. 1-113). National Bureau of Economic Research, Inc.

Perrotti, D. E. (2021). Growth Cycles in Argentina: The recent Behavior. Económica, 67(1), 023. https://doi.org/10.24215/18521649e023

Ravn, M. O. y Uhlig, H. (2002). On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations. The Review of Economics and Statistics, 84(2), 371-376. https://doi.org/10.1162/003465302317411604

Sakarya, N. y de Jong, R. M. (2022). The spectral analysis of the Hodrick-Prescott filter. Journal of Time Series Analysis, 43(3), 479-489. https://doi.org/10.1111/jtsa.12622

Silvia J. (2011). Dynamic Economic Decision Making: Strategies for Financial Risk, Capital Markets, and Monetary Policy. Wiley Finance. https://doi.org/10.1002/9781118273197

Smirnov, S. V., Kondrashov, N. V. y Petronevich, A. V. (2017). Dating cyclical turning points for Russia: Formal methods and informal choices. Journal of Business Cycle Research, 13, 53-73. https://10.1007/s41549-017-0014-9

Zarnowitz, V. y Ozyildirim, A. (2006). Time series decomposition and measurement of business cycles, trends and growth cycles. Journal of Monetary Economics, 53(7), 1717-1739. https://doi.org/10.1016/j.jmoneco.2005.03.015

Downloads

Published

2025-07-08

Issue

Section

Articles

How to Cite

Leiva, F., Cohan, P. P., & Rodriguez, A. (2025). Detection of cycling points using a parametric method: Application of a Markov switching model for the Santa Fe economic cycle. Económica, 71, 042. https://doi.org/10.24215/18521649e042