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Pronósticos para una economía menos volátil: El caso colombiano

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  • Santiago Cajiao Raigosa

    ()

  • Luis Fernando Melo Velandia

    ()

  • Daniel Parra Amado

    ()

Abstract

Este trabajo evalúa si las transformaciones de potencia (Box-Cox y en particular logarítmica) de series de tiempo mejoran la precisión de los pronósticos de modelos ARIMA ajustados a variables económicas de Colombia en dos periodos diferentes: 1980-1995 y 2002-2012. Se compara la habilidad predictiva de series en nivel y series transformadas a través de un experimento fuera de muestra mediante el uso de la prueba de habilidad predictiva incondicional de Giacomini y White [2006]. Se encuentra que los pronósticos de las series transformadas, en general, se desempeñan mejor para el periodo 1980-1995, cuando la economía colombiana fue relativamente más volátil que durante el periodo 2002-2012. Para este último tramo de la muestra, los resultados son mixtos y para algunas series se sugiere mantenerlas en niveles; es decir, sin utilizar transformaciones de potencia.

Suggested Citation

  • Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pronósticos para una economía menos volátil: El caso colombiano," Borradores de Economia 821, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:821
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    References listed on IDEAS

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    1. Juan Pablo Zárate Perdomo & Adolfo León Cobo & José Eduardo Gómez-González, 2012. "Lecciones de las crisis financieras recientes para el diseño e implementación de las políticas monetarias," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 258-293, December.
    2. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    3. Luis Fernando Melo & Héctor Núñez, 2004. "Combinación de Pronósticos de la Inflación en Presencia de cambios Estructurales," Borradores de Economia 286, Banco de la Republica de Colombia.
    4. Jonathan Gillard, 2012. "A generalised Box--Cox transformation for the parametric estimation of clinical reference intervals," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(10), pages 2231-2245, June.
    5. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pronósticos para una economía menos volátil: el caso colombiano," Coyuntura Económica, Fedesarrollo, December.
    6. M. H. Lee & H. J. Sadaei & Suhartono, 2013. "Improving TAIEX forecasting using fuzzy time series with Box--Cox power transformation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2407-2422, November.
    7. Luca Benati, 2003. "Evolving Post-World War II U.K. Economic Performance," Computing in Economics and Finance 2003 171, Society for Computational Economics.
    8. Saikkonen, Pentti & Lütkepohl, Helmut, 2002. "Testing For A Unit Root In A Time Series With A Level Shift At Unknown Time," Econometric Theory, Cambridge University Press, vol. 18(2), pages 313-348, April.
    9. Luetkepohl Helmut & Xu Fang, 2011. "Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-23, February.
    10. Johannes Mayr & Dirk Ulbricht, 2007. "Log versus level in VAR forecasting: 16 Million empirical answers - expect the unexpected," ifo Working Paper Series 42, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. Seaks, Terry G & Layson, Stephen K, 1983. "Box-Cox Estimation with Standard Econometric Problems," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 160-164, February.
    12. Proietti, Tommaso & Lütkepohl, Helmut, 2013. "Does the Box–Cox transformation help in forecasting macroeconomic time series?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 88-99.
    13. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
    14. Proietti, Tommaso & Riani, Marco, 2007. "Transformations and Seasonal Adjustment: Analytic Solutions and Case Studies," MPRA Paper 7862, University Library of Munich, Germany.
    15. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
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    Cited by:

    1. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pronósticos para una economía menos volátil: el caso colombiano," Coyuntura Económica, Fedesarrollo, December.
    2. Davinson Stev Abril Salcedo & Luis Fernando Melo Velandia & Daniel Parra Amado, 2015. "Heterogeneidad de los Índices de Producción Sectoriales de la Industria Colombiana," BORRADORES DE ECONOMIA 012973, BANCO DE LA REPÚBLICA.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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