The Proportion of the Seasonal Period as a Season Index in Weekly Agricultural Data

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

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Proceedings:
International Association of Agricultural Economists Conference (27º .2009. Beijing, China)

Publisher: International Association of Agricultural Economists

Year of publication: 2009

Type: Conference paper

GOOGLE SCHOLAR lock_openOpen access editor

Abstract

In this paper a seasonal model is proposed to deal with weekly agricultural seasonalpatterns in which neither the length of the seasonal period nor the magnitude of theseasonal effects remain the same over time. To model this heterogeneous seasonalbehaviour, the seasonal effect at a season is defined as a function of the proportion of thelength of the seasonal period elapsed up to this season, and the seasonal pattern is modelledby means of evolving splines. The methodology is illustrated for weekly Canary tomatoprices.

Bibliographic References

  • Eubank RL. Spline smoothing and nonparametric regression. M. Dekker: New York; 1988.
  • Ferreira E, Núñez-Antón V, Rodríguez-Póo J. Semiparametric approaches to signal extraction problem in economic time series. Computational Statistics & Data Analysis 2000; 33; 315-333.
  • Harvey AC. Forecasting, Structural Time Series and the Kalman Filter. Cambridge University Press: Cambridge; 1989.
  • Harvey AC, Koopman SJ. Forecasting hourly electricity demand using time varying splines. Journal of the American Statistical Association 1993; 88; 1228-1236.
  • Harvey AC, Koopman SJ, Riani M. The modelling and seasonal adjustment of weekly observations. Journal of Business and Economic Statistics 1997; 15; 354-368.
  • Orbe S, Ferreira E, Rodríguez-Póo J. Nonparametric estimation of time varying parameters under shape restrictions. Journal of Econometrics 2005; 126; 53-77.
  • Pedregal DJ, Young PC. Modulated cycles, and approach to modelling periodic components from rapidly sampled data. International Journal of Forecasting 2006; 22; 181- 194.
  • Poirier DJ. The Econometric of Structural Change with Special Emphasis on Spline Functions. North Holland Publishing Company: Amsterdam; 1976.