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Un Pronóstico no Paramétrico de la Inflación Colombiana

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  • Norberto Rodríguez N.
  • Patricia Siado C.

Abstract

This paper contains the results of a non parametric multi-step ahead forecast for the monthly Colombian inflation, using Mean conditional Kernel estimation over inflation changes, with no inclusion of exogenous variables. The results are compared with those from an ARIMA and a nonlinear STAR. The nonparametric forecast over perform the others two, as well as being the only, from the three, that statistically improved the naive forecast given by a random-walk model.

Suggested Citation

  • Norberto Rodríguez N. & Patricia Siado C., 2003. "Un Pronóstico no Paramétrico de la Inflación Colombiana," Borradores de Economia 248, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:248
    DOI: 10.32468/be.248
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    References listed on IDEAS

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    1. Luis Fernando Melo & Martha Misas, 1998. "Análisis del comportamiento de la inflación trimestral en Colombia bajo cambios de régimen: Una evidencia a través del modelo "Switching" de Hamilton," Revista de Economía del Rosario, Universidad del Rosario, November.
    2. Johnston, Gordon J., 1982. "Probabilities of maximal deviations for nonparametric regression function estimates," Journal of Multivariate Analysis, Elsevier, vol. 12(3), pages 402-414, September.
    3. Martha Misas Arango & Enrique López Enciso & Pablo Querubín Borrero, 2002. "La inflación en Colombia: una aproximación desde las redes neuronales," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 20(41-42), pages 143-214, June.
    4. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    5. Siegfried Heiler, 1999. "A Survey on Nonparametric Time Series Analysis," Finance 9904005, University Library of Munich, Germany.
    6. Heiler, Siegfried, 1999. "A Survey on Nonparametric Time Series Analysis," CoFE Discussion Papers 99/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    7. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, June.
    8. Wolfgang Härdle & Helmut Lütkepohl & Rong Chen, 1997. "A Review of Nonparametric Time Series Analysis," International Statistical Review, International Statistical Institute, vol. 65(1), pages 49-72, April.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Munir A. Jalil & Luis Fernando Melo, 2000. "Una Relación no Líneal entre Inflación y los Medios de Pago," Borradores de Economia 145, Banco de la Republica de Colombia.
    11. Pham, Tuan D. & Tran, Lanh T., 1985. "Some mixing properties of time series models," Stochastic Processes and their Applications, Elsevier, vol. 19(2), pages 297-303, April.
    12. M. Carbon & M. Delecroix, 1993. "Non‐parametric vs parametric forecasting in time series: A computational point of view," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 9(3), pages 215-229, September.
    13. Härdle, Wolfgang & Yang, L., 1996. "Nonparametric Time Series Model Selection," SFB 373 Discussion Papers 1996,53, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    Cited by:

    1. Juan Manuel Julio & Norberto Rodríguez & Héctor Manuel Zárate, 2005. "Estimating the COP Exchange Rate Volatility Smile and the Market Effect of Central Bank Interventions: A CHARN Approach," Borradores de Economia 2605, Banco de la Republica.
    2. Melo, Luis F. & Loaiza, Rubén A. & Villamizar-Villegas, Mauricio, 2016. "Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates," Economic Systems, Elsevier, vol. 40(3), pages 387-397.
    3. Javier Gómez Pineda & María Paola Figueroa, 2003. "Modelo Mensual de Canales de Transmisión," Borradores de Economia 3240, Banco de la Republica.
    4. Luis Eduardo Arango & Luz Adriana Flórez, 2004. "Expectativas de actividad económica en Colombia y estructura a plazo: un poco más de evidencia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(47), pages 126-160, December.

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    More about this item

    Keywords

    Nonparametric forecast; Kernel Estimation; Forecast Evaluation; Bandwidth Selection; Rolling Forecast.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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