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Efectos calendario sobre la producción industrial en Colombia

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  • Luis Fernando Melo Velandia
  • Daniel Parra Amado

Abstract

Este documento estima los efectos calendario sobre la industria manufacturera en Colombia para el periodo comprendido entre enero de 1990 y febrero de 2014. Para ello, se implementaron las metodologías de TRAMO-SEATS de Gómez y Maravall [1994, 1996] y TBATS de De~Livera et al. [2011]. Los resultados muestran que los efectos calendario sobre la industria son significativos, siendo el más relevante la semana santa. Aunque en ambos métodos los coeficientes asociados a dichos efectos impactan negativamente la producción industrial, en TRAMO-SEATS la magnitud de ellos es mayor que la estimada por TBATS. Se encuentra que la diferencia entre las tasas de crecimiento anual de los métodos cuando se modelan los efectos calendario respecto a la serie original son, en promedio, 1,36% para el primero y 2,82% para el segundo. Por último, la semana santa tiene un impacto promedio de 5,13% y 4,60 %, respectivamente.

Suggested Citation

  • Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Efectos calendario sobre la producción industrial en Colombia," Borradores de Economia 820, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:820
    DOI: 10.32468/be.820
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    References listed on IDEAS

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    1. Alysha M De Livera & Rob J Hyndman, 2009. "Forecasting time series with complex seasonal patterns using exponential smoothing," Monash Econometrics and Business Statistics Working Papers 15/09, Monash University, Department of Econometrics and Business Statistics.
    2. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    3. Villarreal, Francisco G., 2005. "Elementos teóricos del ajuste estacional de series económicas utilizando X-12-ARIMA y TRAMO-SEATS," Estudios Estadísticos 4741, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    4. Tucker McElroy, 2011. "A nonparametric method for asymmetrically extending signal extraction filters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 597-621, November.
    5. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    6. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    7. Agustín Maravall Herrero & Domingo Pérez Cañete, 2011. "Applying and interpreting model-based seasonal adjustment. The euro-area industrial production series," Working Papers 1116, Banco de España.
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    Cited by:

    1. 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 888, Banco de la Republica de Colombia.

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    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
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

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