Forecasting monthly fisheries prices: Model comparison using data from Cornwall (UK)

Christos Floros, Pierre Failler

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Forecasting has a key role in applied economics and management of fisheries. In this paper we report the forecasting competition between Autoregressive AR(p), Moving Average MA(q) and ARMA(p,q) models of the monthly average fisheries prices. We consider twelve species landed into Cornwall: Anglerfish, Cod, Crabs, Dogfish, Haddock, Hake, Lemonsole, Mackerel, Plaice, Saithe, Sole and Whiting. In our evaluation of the out-of-sample forecasting accuracy of ten models, we show that simple ARMA(p,q) models generally prove to be the best forecasting models.

Original languageEnglish
Pages (from-to)613-624
Number of pages12
JournalEuropean Journal of Scientific Research
Volume14
Issue number4
Publication statusPublished - 2006

Keywords

  • Cornwall (UK)
  • Fisheries prices
  • Forecasting
  • Time series models

Fingerprint Dive into the research topics of 'Forecasting monthly fisheries prices: Model comparison using data from Cornwall (UK)'. Together they form a unique fingerprint.

  • Cite this