IDEAS home Printed from https://ideas.repec.org/a/rbp/esteco/ree-26-03.html
   My bibliography  Save this article

Construyendo un índice coincidente de recesión: Una aplicación para la economía peruana

Author

Listed:
  • Mendoza, Liu
  • Morales, Daniel

Abstract

¿Una caída en la producción implica una desaceleración temporal o marca el inicio de una recesión? Para responder esta pregunta, en este documento se construye un índice probabilístico coincidente mensual para detectar recesiones en la economía peruana usando un modelo no lineal del tipo Markov-switching. En la construcción de este índice se pone énfasis al contenido informativo de encuestas a consumidores y empresarios, así como variables reales y financieras internacionales. El índice final detecta con prontitud y confiabilidad el período recesivo asociado con la crisis financiera internacional de 2008-2009, incluso en un análisis en tiempo real. Sin embargo, debido a que este índice ha sido elaborado con información limitada debido a la poca disponibilidad de datos, su capacidad de detección de recesiones futuras aún debe sobrepasar la prueba del tiempo.

Suggested Citation

  • Mendoza, Liu & Morales, Daniel, 2013. "Construyendo un índice coincidente de recesión: Una aplicación para la economía peruana," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 81-100.
    • Handle: RePEc:rbp:esteco:ree-26-03
    as

    Download full text from publisher

    File URL: https://www.bcrp.gob.pe/docs/Publicaciones/Revista-Estudios-Economicos/26/ree-26-mendoza-morales.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Bruce E, 1992. "The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 61-82, Suppl. De.
    2. Hwee Kwan Chow & Keen Meng Choy, 2009. "Analyzing and forecasting business cycles in a small open economy: A dynamic factor model for Singapore," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2009(1), pages 19-41.
    3. Oscar Dancourt & Waldo Mendoza, 2009. "Choque externo y respuestas de política macroeconómica," Capítulos de Libros PUCP / Chapters of PUCP books, in: Oscar Dancourt & Félix Jiménez (ed.), Crisis internacional. Impactos y respuestas de política económica en el Perú., edition 1, chapter 2, pages 59-88, Fondo Editorial - Pontificia Universidad Católica del Perú.
    4. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
    5. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    6. Carrera Cesar, 2012. "Estimating Information Rigidity Using Firms' Survey Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-34, June.
    7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    8. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    9. Gabriel Rodríguez, 2010. "Application of Three Non-Linear Econometric Approaches to Identify Business Cycles in Peru," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-25.
    10. Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, University Library of Munich, Germany.
    11. Javier Escobal & Javier Torres, 2002. "Un Sistema de Indicadores Líderes del Nivel de Actividad para la Economía Peruana," Documentos de Investigación dt39, Grupo de Análisis para el Desarrollo (GRADE).
    12. Etter, Richard & Graff, Michael, 2011. "A composite leading indicator for the Peruvian economy based on the BCRP's monthly business tendency surveys," Working Papers 2011-006, Banco Central de Reserva del Perú.
    13. John B. Taylor, 2009. "The Financial Crisis and the Policy Responses: An Empirical Analysis of What Went Wrong," NBER Working Papers 14631, National Bureau of Economic Research, Inc.
    14. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2010. "Green shoots in the euro area. A real time measure," Working Papers 1026, Banco de España.
    15. Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A Measure Of Comovement For Economic Variables: Theory And Empirics," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 232-241, May.
    16. Jacques Anas & Laurent Ferrara, 2004. "Detecting Cyclical Turning Points: The ABCD Approach and Two Probabilistic Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(2), pages 193-225.
    17. Neftici, Salih N., 1982. "Optimal prediction of cyclical downturns," Journal of Economic Dynamics and Control, Elsevier, vol. 4(1), pages 225-241, November.
    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. Mendoza, Liu & Morales, Daniel, 2012. "Constructing a real-time coincident recession index: an application to the Peruvian economy," Working Papers 2012-020, Banco Central de Reserva del Perú.
    2. Benoit Bellone, 2004. "Une lecture probabiliste du cycle d’affaires américain," Econometrics 0407002, University Library of Munich, Germany, revised 28 Mar 2005.
    3. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    4. Benoît Bellone, 2006. "Une lecture probabiliste du cycle d’affaires américain," Économie et Prévision, Programme National Persée, vol. 172(1), pages 63-81.
    5. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    6. Antonin Aviat & Frédérique Bec & Claude Diebolt & Catherine Doz & Denis Ferrand & Laurent Ferrara & Eric Heyer & Valérie Mignon & Pierre-Alain Pionnier, 2021. "Dating business cycles in France: a reference chronology," SciencePo Working papers Main hal-03373425, HAL.
    7. Maria Gadea & Ana Gómez-Loscos & Antonio Montañés, 2012. "Cycles inside cycles: Spanish regional aggregation," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(4), pages 423-456, December.
    8. Imed Medhioub, 2010. "Business Cycle Synchronization: A Mediterranean Comparison," Working Papers 527, Economic Research Forum, revised 06 Jan 2010.
    9. Sébastien Le Coent & Erwan Gautier & Benoît Bellone, 2006. "Les marchés financiers anticipent-ils les retournements conjoncturels ?," Économie et Prévision, Programme National Persée, vol. 172(1), pages 83-99.
    10. Medhioub, Imed, 2007. "Asymétrie des cycles économiques et changement de régimes : cas de la Tunisie," L'Actualité Economique, Société Canadienne de Science Economique, vol. 83(4), pages 529-553, décembre.
    11. Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, University Library of Munich, Germany.
    12. Sergey V. Smirnov & Nikolay V. Kondrashov & Anna V. Petronevich, 2017. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 53-73, May.
    13. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    14. Mike Artis & Hans-Martin Krolzig & Juan Toro, 2004. "The European business cycle," Oxford Economic Papers, Oxford University Press, vol. 56(1), pages 1-44, January.
    15. Olivier Darné & Laurent Ferrara, 2011. "Identification of Slowdowns and Accelerations for the Euro Area Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(3), pages 335-364, June.
    16. Chang-Jin Kim & Jeremy M. Piger & Richard Startz, 2001. "Permanent and transitory components of business cycles: their relative importance and dynamic relationship," International Finance Discussion Papers 703, Board of Governors of the Federal Reserve System (U.S.).
    17. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    18. Sun, Jiandong & Feng, Shuaizhang & Hu, Yingyao, 2021. "Misclassification errors in labor force statuses and the early identification of economic recessions," Journal of Asian Economics, Elsevier, vol. 75(C).
    19. Morais, Igor Alexandre C. & Chauvet, Marcelle, 2011. "Leading Indicators for the Capital Goods Industry," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.
    20. Yasutomo Murasawa & Roberto S. Mariano, 2004. "Constructing a Coincident Index of Business Cycles Without Assuming a One-Factor Model," Econometric Society 2004 Far Eastern Meetings 710, Econometric Society.

    More about this item

    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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:rbp:esteco:ree-26-03. 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: Departamento de Publicaciones Económicas (email available below). General contact details of provider: https://edirc.repec.org/data/bcrgvpe.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.