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The real yield curve and macroeconomic factors in the Chilean economy


  • Marco Morales


This article estimates a dynamic model for the yield curve incorporating latent and macro factors to represent the term structure of the real interest rates. The representation of the yield curve is based on the popular latent factor model of Nelson and Siegel (1987), but under a dynamic interpretation due to Diebold and Li (2006). After assuming the data generating process for the latent and macro factors can be represented by a VAR process, the yields-macro model can be regarded as a state-space representation and estimated by a Kalman Filter approach or by using a simplified two-step procedure proposed by Diebold and Li (2006). This article follows the simple two-step method and makes a comparison check with the Kalman Filter estimation, concluding that the basic intuition of the results is not significantly affected by the use of the simplified approach. Estimation results give support to the dynamic interaction between yield curve latent factors and macroeconomic variables. In particular, monetary policy implemented by the Central Bank seems to be influenced by the market players given the significant response of the monetary policy rate to the yield curve factors as shown by impulse-response functions. In addition, the level and slope of the yield curve seems to be responsive to real activity and monetary policy shocks, issues that should be considered by monetary authorities given the dependency of monetary policy effectiveness on the shape of the yield curve.

Suggested Citation

  • Marco Morales, 2010. "The real yield curve and macroeconomic factors in the Chilean economy," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3533-3545.
  • Handle: RePEc:taf:applec:v:42:y:2010:i:27:p:3533-3545
    DOI: 10.1080/00036840802129806

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    References listed on IDEAS

    1. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    2. Taylor, Mark P, 1992. "Modelling the Yield Curve," Economic Journal, Royal Economic Society, vol. 102(412), pages 524-537, May.
    3. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    4. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    5. Luis Oscar Herrera & Igal Magendzo, 1997. "Expectativas Financieras y la Curva de Tasas Forward de Chile," Working Papers Central Bank of Chile 23, Central Bank of Chile.
    6. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    7. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
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    Cited by:

    1. Rodrigo Alfaro A., 2013. "Yield Curve Modeling And Forecasting: The Dynamic Nelson-Siegel Approach. Francis X. Diebold and Glenn D. Rudebusch," Revisión de libros Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 16(2), pages 150-153, August.
    2. Bautista, Rafaél & Riáscos, Álvaro & Suárez, Nicolás, 2007. "La aplicación de un modelo de factores a las curvas de rendimiento del mercado de deuda pública colombiano," Galeras. Working Papers Series 014, Universidad de Los Andes. Facultad de Administración. School of Management.
    3. Rodrigo Alfaro, 2009. "The Yield Curve Under Nelson-Siegel," Working Papers Central Bank of Chile 531, Central Bank of Chile.
    4. Rodrigo Alfaro & Antonio Fernandois & Andrés Sagner, 2018. "Expectativas Financieras y Tasas Forward en Chile," Working Papers Central Bank of Chile 814, Central Bank of Chile.
    5. Alfaro, Rodrigo & Becerra, Juan Sebastian & Sagner, Andres, 2010. "Estimación de la estructura de tasas utilizando el modelo Dinámico Nelson Siegel: resultados para Chile y EEUU [The Dynamic Nelson-Siegel model: empirical results for Chile and US]," MPRA Paper 25912, University Library of Munich, Germany, revised 23 Jun 2010.
    6. Luis Ceballos & Alberto Naudon & Damián Romero, 2016. "Nominal term structure and term premia: evidence from Chile," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2721-2735, June.
    7. Sowmya, Subramaniam & Prasanna, Krishna, 2018. "Yield curve interactions with the macroeconomic factors during global financial crisis among Asian markets," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 178-192.
    8. J.Marcelo Ochoa, 2006. "An interpretation of an affine term structure model of Chile," Estudios de Economia, University of Chile, Department of Economics, vol. 33(2 Year 20), pages 155-184, December.
    9. Samuel Carrasco & Luis Ceballos & Jessica Mena, 2016. "Estimación de la estructura de tasas de interés en Chile," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 19(1), pages 58-75, April.
    10. Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & Muhammed Hasan Yilmaz, 2018. "The Interaction between Yield Curve and Macroeconomic Factors," CBT Research Notes in Economics 1802, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    11. Krishna Prasanna & Subramaniam Sowmya, 2017. "Yield curve in India and its interactions with the US bond market," International Economics and Economic Policy, Springer, vol. 14(2), pages 353-375, April.

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