218
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Demand systems for agricultural products in OECD countries

Pages 163-169 | Published online: 20 Aug 2006
 

Abstract

This study concerned with the estimation of demand systems for agricultural products in OECD countries. Three representatives demand systems with their extensions, namely the Rotterdam Model, An Almost Ideal Demand System (AIDS), and CBS model are used. These models are estimated by Seemingly Unrelated Regression (SUR) method. The procedures to estimate demand systems suggest significant empirical regularities for agricultural products in OECD countries. The study also applies a procedure for model selection. This procedure implies the superiority of AIDS and CBS models over the Rotterdam model. The main contribution of this study is to model demand for agricultural products over a wide array of items and across large number of countries.

Notes

1 For a wider discussion on the likelihood ratio test for model selection, see Amemiya (Citation1985).

2 For a wider discussion on estimation methods for demand systems, see Edgerton et al. (Citation1996).

3 The diagnostics (Breusch-Pagan LM test for heteroscedasticity and Durbin-Watson test of autocorrelation) show that the models are generally robust. The results of the diagnostics can be requested from the author.

4 All the parameter estimates not reported here will be available upon the request from the author.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 205.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.