IDEAS home Printed from https://ideas.repec.org/p/apk/doctra/2501.html
   My bibliography  Save this paper

Growt-at-risk in Costa Rica: an Open and Small Economy Perspective

Author

Listed:
  • Carlos Segura-Rodriguez

    (Department of Economic Research, Central Bank of Costa Rica)

  • David Ching-Vindas

    (Department of Economic Research, Central Bank of Costa Rica)

Abstract

This paper presents a new estimation of a Financial Conditions Index (FCI) and, following Adrian et al. (2019), the first growth-at-risk analysis for the Costa Rican economy. The FCI is constructed using the dynamic factor model technique since 1996. For those variables that are reported after this date, we use Stock y Watson (2002) proposal to fill for the missing values. The FCI effectively captures recent episodes of restrictive and lax financial conditions. The growth-at-risk analysis incorporates the impact of terms of trade to account for international risks relevant to a small, open economy like Costa Rica. The results show that, at one and four quarters ahead, both restrictive financial conditions and improvements in terms of trade have a negative and statistically significant effect on the 5th percentile of growth, but not on other percentiles or on the expected value. This underscores the importance of assessing how financial conditions and terms of trade influence risks to future economic growth. ***Resumen: Este trabajo presenta una nueva estimación de un Índice de Condiciones Financieras (ICF) y, con base en Adrian, Boyarchenko y Giannone (2019), el primer análisis de crecimiento en riesgo (Growth-at-Risk) para la economía costarricense. El ICF se construye con la metodología de factores dinámicos a partir de 1996. Se utiliza la propuesta de Stock y Watson (2002) para incluir variables para las que se tiene información para años posteriores. El ICF replica de manera apropiada los episodios recientes de condiciones financieras restrictivas y laxas. En el análisis de crecimiento en riesgo, se incluyó el impacto de los términos de intercambio para captar riesgos internacionales relevantes para una economía pequeña y abierta como Costa Rica. Los resultados indican que, a un trimestre y a cuatro trimestres, tanto las condiciones financieras restrictivas como la mejora en los términos de intercambio tienen un efecto negativo y estadísticamente significativo para el percentil 5, pero no para los demás percentiles ni en el nivel promedio. Esto subraya la importancia de evaluar cómo las condiciones financieras y los términos de intercambio influyen en los riesgos para el crecimiento económico futuro.

Suggested Citation

  • Carlos Segura-Rodriguez & David Ching-Vindas, 2025. "Growt-at-risk in Costa Rica: an Open and Small Economy Perspective," Documentos de Trabajo 2501, Banco Central de Costa Rica.
  • Handle: RePEc:apk:doctra:2501
    as

    Download full text from publisher

    File URL: https://repositorioinvestigaciones.bccr.fi.cr/handle/20.500.12506/500
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tobias Adrian & Federico Grinberg & Nellie Liang & Sheheryar Malik & Jie Yu, 2022. "The Term Structure of Growth-at-Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 283-323, July.
    2. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    3. Ossandon Busch, Matias & Sánchez-Martínez, José Manuel & Rodríguez-Martínez, Anahí & Montañez-Enríquez, Ricardo & Martínez-Jaramillo, Serafín, 2022. "Growth at risk: Methodology and applications in an open-source platform," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
    4. Scott Brave & R. Andrew Butters, 2012. "Diagnosing the Financial System: Financial Conditions and Financial Stress," International Journal of Central Banking, International Journal of Central Banking, vol. 8(2), pages 191-239, June.
    5. María Victoria Landaberry & Rodrigo Lluberas & Micaela Vidal, 2021. "Una aplicación de la metodología Growth at Risk a Uruguay," Documentos de trabajo 2021009, Banco Central del Uruguay.
    6. Wilmar Cabrera & Jorge Hurtado & Miguel Morales & Juan Sebastián Rojas, 2014. "A Composite Indicator of Systemic Stress (CISS) for Colombia," Temas de Estabilidad Financiera 80, Banco de la Republica de Colombia.
    7. Kathryn Holston & Thomas Laubach & John C. Williams, 2023. "Measuring the Natural Rate of Interest after COVID-19," Staff Reports 1063, Federal Reserve Bank of New York.
    8. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    9. repec:hal:journl:peer-00844811 is not listed on IDEAS
    10. Diego Chicana & Rafael Nivin, 2021. "Evaluating Growth-at-Risk as a tool for monitoring macro-financial risks in the Peruvian economy," IHEID Working Papers 07-2021, Economics Section, The Graduate Institute of International Studies.
    11. John C. Williams, 2023. "Measuring the Natural Rate of Interest: Past, Present, and Future," Speech 96178, Federal Reserve Bank of New York.
    12. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    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. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.
    2. Raluca Maran, 2023. "Impact of macroprudential policy on economic growth in Indonesia: a growth-at-risk approach," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(3), pages 575-613, December.
    3. Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020. "Financial Variables as Predictors of Real Growth Vulnerability," Documents de Travail de l'OFCE 2020-06, Observatoire Francais des Conjonctures Economiques (OFCE).
    4. repec:hal:spmain:info:hdl:2441/4nn4ojjkth8qe9ci5b0hpu7ala is not listed on IDEAS
    5. repec:spo:wpmain:info:hdl:2441/4nn4ojjkth8qe9ci5b0hpu7ala is not listed on IDEAS
    6. Hyeongwoo Kim & Wen Shi & Hyun Hak Kim, 2020. "Forecasting financial stress indices in Korea: a factor model approach," Empirical Economics, Springer, vol. 59(6), pages 2859-2898, December.
    7. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    8. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    9. Schick, Manuel, 2024. "Real-time Nowcasting Growth-at-Risk using the Survey of Professional Forecasters," Working Papers 0750, University of Heidelberg, Department of Economics.
    10. Kamber, Güneş & Morley, James & Wong, Benjamin, 2025. "Trend-cycle decomposition in the presence of large shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 173(C).
    11. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
    12. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    13. Tommaso Proietti, 2008. "Estimation of Common Factors Under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and Its Main Components," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 547-558, Springer.
    14. Thomas Despois & Catherine Doz, 2023. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 533-555, June.
    15. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    16. Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2018. "Dynamic factor model with infinite‐dimensional factor space: Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 625-642, August.
    17. Karen Poghosyan & Ruben Poghosyan, 2021. "On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(1), pages 52-79, June.
    18. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.
    19. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    20. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
    21. Adams, Patrick A. & Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2021. "Forecasting macroeconomic risks," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1173-1191.
    22. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:apk:doctra:2501. 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 Investigación Económica (email available below). General contact details of provider: https://edirc.repec.org/data/bccrrcr.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.