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Forecasting the spot prices of various coffee types using linear and non-linear error correction models

Author

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  • Costas Milas

    ()

  • Jesus Otero

    ()

  • Theodore Panagiotidis

    ()

Abstract

This paper estimates linear and non-linear error correction models for the spot prices of four different coffee types. In line with economic priors, we find some evidence that when prices are too high, they move back to equilibrium more slowly than when they are too low. This may reflect the fact that, in the short run, it is easier for countries to restrict the supply of coffee in order to raise prices, rather than increase supply in order to reduce them. Further, there is some evidence that adjustment is faster when deviations from the equilibrium level get larger. Our forecasting analysis suggests that asymmetric and non-linear error correction models offer weak evidence of improved forecasting performance relative to the random walk model. *********************************************************************** Este documento estima modelos lineales y no-lineales de corrección de errores para los precios spot de cuatro tipos de café. En concordancia con las leyes económicas, se encuentra evidencia que cuando los precios están por encima de su nivel de equilibrio, retornan a éste mas lentamente que cuando están por debajo. Esto puede reflejar el hecho que, en el corto plazo, para los países productores de café es mas fácil restringir la oferta para incrementar precios, que incrementarla para reducirlos. Además, se encuentra evidencia que el ajuste es más rápido cuando las desviaciones del equilibrio son mayores. Los pronósticos que se obtienen a partir de los modelos de corrección de errores no lineales y asimétricos considerados en el trabajo, ofrecen una leve mejoría cuando se comparan con los pronósticos que resultan de un modelo de paseo aleatorio.

Suggested Citation

  • Costas Milas & Jesus Otero & Theodore Panagiotidis, 2001. "Forecasting the spot prices of various coffee types using linear and non-linear error correction models," BORRADORES DE INVESTIGACIÓN 002737, UNIVERSIDAD DEL ROSARIO.
  • Handle: RePEc:col:000091:002737
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    References listed on IDEAS

    as
    1. Costas Milas & Jesus Otero, 2002. "Smooth transition vector error correction models for the spot prices of coffee," Applied Economics Letters, Taylor & Francis Journals, vol. 9(14), pages 925-928.
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    Cited by:

    1. Angelos Kanas, 2008. "Modeling regime transition in stock index futures markets and forecasting implications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 649-669.
    2. Bastianin, Andrea & Lanza, Alessandro & Manera, Matteo, 2016. "Economic Impacts of El Niño Southern Oscillation: Evidence from the Colombian Coffee Market," EIA: Climate Change: Economic Impacts and Adaptation 250258, Fondazione Eni Enrico Mattei (FEEM).
    3. Li, Xi-Le & Saghaian, Sayed, 2014. "The Presence Of Market Power In The Coffee Market: The Case Of Colombian Milds," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170348, Agricultural and Applied Economics Association.
    4. David Ubilava, 2012. "El Niño, La Niña, and world coffee price dynamics," Agricultural Economics, International Association of Agricultural Economists, vol. 43(1), pages 17-26, January.
    5. John M. Fry & Baoying Lai, 2011. "The interdependence of Coffee spot and futures markets," Working Papers 2011.1, International Network for Economic Research - INFER.
    6. Benedicto Lukanima & Raymond Swaray, 2014. "Market Reforms and Commodity Price Volatility: The Case of East African Coffee Market," The World Economy, Wiley Blackwell, vol. 37(8), pages 1152-1185, August.
    7. Coronado Ramírez Semei Leopoldo & Porras Serrano Jesús & Sandoval Bravo Salvador, 2013. "Aplicación de bicorrelación cruzada al rendimiento diario del precio del café," Contaduría y Administración, Accounting and Management, vol. 58(1), pages 117-129, enero-mar.

    More about this item

    Keywords

    Coffee prices; asymmetric and non-linear error correction models; forecasting;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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