Heterogeneous seasonal patterns in agricultural data and evolving splines

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

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
IUP Journal of Agricultural Economics

ISSN: 0973-2276

Año de publicación: 2007

Volumen: 4

Páginas: 48-65

Tipo: Artículo

Otras publicaciones en: IUP Journal of Agricultural Economics

Resumen

In this paper an appropriate model of the seasonal pattern in high frequencyagricultural data is proposed that takes the specific nature of such a pattern into account.The methodological proposal is based on evolving splines that are shown to be a toolcapable of modelling seasonal variations in which either the period or the magnitude ofthe seasonal fluctuations do not remain the same over time. The seasonal pattern in eachyear or agricultural campaign is modelled in such a way that the seasonal effect at eachseason is a function of the seasonal effects corresponding to some fixed seasons that actas reference points. The spline function is enforced to satisfy several conditions thatprovide some regularity in the adjusted seasonal fluctuation; on the other hand, the mainsource of changes in the adjusted seasonal pattern is obtained by assuming that thevalues of the seasonal effects at the fixed reference seasons do not remain the same yearby year. If the length of the period in which the seasonal fluctuation is completed doesnot change, the proposed specification is flexible enough to test the hypothesis that theseasonal pattern in several consecutive years is fixed by using simple statisticalprocedures. This proposal is applied to capture the movements in a weekly tomatoexport series and the analysis is carried out inside the frame delimited by the structuralapproach to time series

Referencias bibliográficas

  • Harvey, AC (1989). Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press.
  • Harvey, AC, Koopman, SJ and Riani, M (1997). ‘The modelling and seasonal adjustment of weekly observations’, Journal of Business and Economic Statistics, 15, pp. 354-368.
  • Hill, HSJ, Mjelde, JW, Love, HA, Rubas, DJ, Fuller, SW, Rosenthal, W and Hammer, G (2004). ‘Implications of seasonal climate forecast on world wheat trade: a stochastic dynamic analysis’, Canadian Journal of Agricultural Economics-Revue Canadienne d Agroeconomie, 52, pp. 289-312.
  • Jarvis, L and Vera-Toscano, E (2004). ‘Seasonal adjustment in a market for femate agricultural workers’, American Journal of Agricultural Economics, 86, pp. 254-266.
  • Koopman, SJ (1992). ‘Diagnostic Checking and Intra-Daily Effects in Time Series Models’. Thesis Publishers, Tinbergen Institute Research Series, 27. Amsterdam.
  • Martín, G and Cáceres, JJ (2005). ‘Modelling weekly Canary tomato exports’, Agricultural Economics, 33, pp. 255-267.
  • Miller, DJ and Hayenga, ML (2001). ‘Price cycles and asymmetric price transmission in the US pork market’, American Journal of Agricultural Economics, 83, pp. 551-562.
  • Poirier, DJ (1976). The Econometric of Structural Change with special emphasis on Spline Functions, Amsterdam, North Holland.
  • Richards, TJ and Patterson, PM (2005). ‘Retail price fixity as a facilitating mechanism’, American Journal of Agricultural Economics, 87, pp. 85-102.
  • Rucker, RR, Thurman, WN and Yoder, JK (2005). ‘Estimating the structure of market reaction to news: information events and lumber future prices’, American Journal of Agricultural Economics, 87, pp. 482-500.
  • Sanjuán, AI and Dawson, PJ (2003). ‘Price transmission: BSE and structural breaks in the UK meat sector’, European Review of Agricultural Economics, 30, pp. 155-172.
  • Sorensen, C (2002). ‘Modeling seasonality in agricultural commodity futures’, Journal of Futures Markets, 22, pp. 393-426.