Evolving Seasonal Pattern of Tenerife Tomato Exports

  1. José Juan Cáceres Hernández 1
  2. Carmen Gloria Martín Rodríguez 1
  1. 1 Universidad de La Laguna

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    GRID grid.10041.34

International Congress of European Association of Agricultural Economists (11. Copenhagen. 2005)

Year of publication: 2005

Type: Conference paper

Export: RIS
DOI: 10.22004/ag.econ.24501 GOOGLE SCHOLAR lock_openOpen access editor


The aim of this paper is to analyse the long term movements and, particularly, the seasonal pattern of Tenerife (Canary Islands) tomato exports throughout the last two decades. In order to observe more clearly the exporter's decisions, weekly data has been used. The instabilities in the long term behaviour of the series and the specific nature of the seasonal pattern should be taken into account in order to capture the performance of exports accurately. Thus, this analysis is carried out inside the frame delimited by the structural approach to time series and the usefulness of evolving splines as a tool capable of modelling seasonal variations in which either the period or the magnitude of the fluctuations do not remain the same over time is shown.

Bibliographic References

  • Cáceres, J.J. (2000). La Exportación de Tomate en Canarias. Elementos para una estrategia competitiva. Ediciones Canarias.
  • Cáceres, J.J. (2001). Optimalidad del patrón estacional de las exportaciones canarias de tomate. Estudios de Economía Aplicada 18: 41-66.
  • Durbin, J. and Koopman, S.J. (2001). Time Series Analysis by State Space Methods. Oxford University Press.
  • Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
  • Harvey, A.C., Koopman, S.J. and Riani, M. (1997). The modelling and seasonal adjustment of weekly observations. Journal of Business and Economic Statistics 15(3): 354-368.
  • Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Transactions ASME, Series D, Journal of Basic Engineering 82: 35-45.
  • Kalman, R.E. and Bucy, R.S. (1961). New results in linear filtering and prediction theory. Transactions ASME, Series D, Journal of Basic Engineering 83: 95-108.
  • Koopman, S.J. (1992). Diagnostic Checking and Intra-Daily Effects in Time Series Models. Thesis Publishers, Tinbergen Institute Research Series, 27. Amsterdam.
  • Koopman, S.J., Harvey, A.C., Doornik, J.A. and Shephard, N. (2000). STAMP: Structural Time Series Analyser, Modeller and Predictor. Timberlake Consultants.
  • Marsh, L. (1983). On estimating spline regression. Proceedings of SAS User’s Group International 8: 723- 728.
  • Marsh, L. (1986). Estimating the number and location of knots in spline regression. Journal of Applied Business Research 3: 60-70.
  • Marsh, L., Maudgal, M. and Raman, J. (1990). Alternative methods of estimating piecewise linear and higher order regression models. Proceedings of SAS User’s Group International 15: 523-527.
  • Martín, G., Cano, V. and Cáceres, J.J. (2002). Exportación de tomate en Canarias: ¿un patrón estacional estable? Economía Agraria y Recursos Naturales 2(2): 53-72.
  • Martín, G. and Cáceres, J.J. (2004). Modelling weekly Canary tomato exports. Agricultural Economics (forthcoming).
  • Nielsen, H.B. (1998). Cubic Splines. IMM Department of Mathematical Modelling. Technical University of Denmark.
  • Poirier, D.J. (1973). Piecewise regression using cubic splines. Journal of the American Statistical Association 68: 515-524.
  • Poirier, D.J. (1976). The Econometrics of Structural Change with Special Emphasis on Spline Functions. North-Holland.