The Application of Robust Regression to a Production Function Comparison – the Example of Swiss Corn
The adequate representation of crop response functions is crucial for agricultural modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms. In this article, the perspective is expanded by also considering an alternative regression method. This is motivated by the fact that extreme climatic events can result in crop yield observations that cause misleading results if Least Squares regression is applied. We show that such outliers are adequately treated if and only if robust regression or robust diagnostics are applied. The example of simulated Swiss corn yields shows that the application of robust instead of Least Squares regression causes reasonable shifts in coefficient estimates and their level of significance, and results in higher levels of goodness of fit. Furthermore, the costs of misspecification decrease remarkably if optimal input recommendations are based on results of robust regression. We therefore recommend the application of the latter instead of Least Squares regression for agricultural and environmental production function estimation.
|Date of creation:||Sep 2007|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jan-Egbert Sturm & Jakob De Haan, 2001. "How robust is the relationship between economic freedom and economic growth?," Applied Economics, Taylor & Francis Journals, vol. 33(7), pages 839-844.
- Rajsic, Predrag & Weersink, Alfons, 2008. "Do farmers waste fertilizer? A comparison of ex post optimal nitrogen rates and ex ante recommendations by model, site and year," Agricultural Systems, Elsevier, vol. 97(1-2), pages 56-67, April.
- Meinke, H. & Baethgen, W. E. & Carberry, P. S. & Donatelli, M. & Hammer, G. L. & Selvaraju, R. & Stockle, C. O., 2001. "Increasing profits and reducing risks in crop production using participatory systems simulation approaches," Agricultural Systems, Elsevier, vol. 70(2-3), pages 493-513.
- Fuchs, C., 1996. "Einfluss der Form von Produktionsfunktionen auf die Ermittlung der optimalen speziellen IntensitÃ¤t und die Ã¶kologische Wirkungen in der Pflanzenproduktion," Proceedings "Schriften der Gesellschaft fÃ¼r Wirtschafts- und Sozialwissenschaften des Landbaues e.V.", German Association of Agricultural Economists (GEWISOLA), vol. 32.
- Godard, C. & Roger-Estrade, J. & Jayet, P.A. & Brisson, N. & Le Bas, C., 2008. "Use of available information at a European level to construct crop nitrogen response curves for the regions of the EU," Agricultural Systems, Elsevier, vol. 97(1-2), pages 68-82, April.
- Llewelyn, Richard V. & Featherstone, Allen M., 1997. "A comparison of crop production functions using simulated data for irrigated corn in western Kansas," Agricultural Systems, Elsevier, vol. 54(4), pages 521-538, August.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:4740. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.