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Panel Data Estimation Techniques for Farm-level Data Model

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Author Info
Platoni, S.
Sckokai, P.
Moro, D.

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Abstract

Econometric models wishing to estimate relevant parameters for agricultural policy analysis are increasingly relying on unbalanced panels of farm-level data. Since in the agricultural economics literature such models have often been estimated through simplified approaches, in this paper we try to verify whether the adoption of more sophisticated panel data techniques may impact the estimation results. For this reason, the policy model by Moro and Sckokai (1999) has been reestimated using techniques recently developed in the econometric literature. The preliminary results show a strong impact on the estimations. This seems to suggest that the adoption of proper panel-data techniques is likely to be very important in order to obtain reliable estimates of some key policy parameters.

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Publisher Info
Paper provided by European Association of Agricultural Economists in its series 2008 International Congress, August 26-29, 2008, Ghent, Belgium with number 44268.

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Date of creation: 2008
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Handle: RePEc:ags:eaae08:44268

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Related research
Keywords: Agricultural policy; Panel data; Systems of equations; Agricultural and Food Policy;

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References listed on IDEAS
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  1. Biorn, Erik, 2004. "Regression systems for unbalanced panel data: a stepwise maximum likelihood procedure," Journal of Econometrics, Elsevier, vol. 122(2), pages 281-291, October. [Downloadable!] (restricted)
  2. Randolph, William C., 1988. "A transformation for heteroscedastic error components regression models," Economics Letters, Elsevier, vol. 27(4), pages 349-354. [Downloadable!] (restricted)
  3. Paolo Sckokai & Daniele Moro, 2006. "Modeling the Reforms of the Common Agricultural Policy for Arable Crops under Uncertainty," American Journal of Agricultural Economics, American Agricultural Economics Association, vol. 88(1), pages 43-56, 02. [Downloadable!] (restricted)
  4. Paolo Sckokai & Jesús Antón, 2005. "The Degree of Decoupling of Area Payments for Arable Crops in the European Union," American Journal of Agricultural Economics, American Agricultural Economics Association, vol. 87(5), pages 1220-1228, November. [Downloadable!] (restricted)
  5. Davis, Peter, 2002. "Estimating multi-way error components models with unbalanced data structures," Journal of Econometrics, Elsevier, vol. 106(1), pages 67-95, January. [Downloadable!] (restricted)
  6. Li, Qi & Stengos, Thanasis, 1994. "Adaptive Estimation in the Panel Data Error Component Model with Heteroskedasticity of Unknown Form," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(4), pages 981-1000, November. [Downloadable!] (restricted)
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  7. H. Baltagi, Badi & Heun Song, Seuck & Cheol Jung, Byoung, 2001. "The unbalanced nested error component regression model," Journal of Econometrics, Elsevier, vol. 101(2), pages 357-381, April. [Downloadable!] (restricted)
  8. Teresa Serra & David Zilberman & Barry K. Goodwin & Allen Featherstone, 2006. "Effects of decoupling on the mean and variability of output," European Review of Agricultural Economics, Oxford University Press for the Foundation for the European Review of Agricultural Economics, vol. 33(3), pages 269-288, September.
  9. Nilanjana Roy, 2002. "Is Adaptive Estimation Useful For Panel Models With Heteroskedasticity In The Individual Specific Error Component? Some Monte Carlo Evidence," Econometric Reviews, Taylor and Francis Journals, vol. 21(2), pages 189-203. [Downloadable!] (restricted)
  10. Wansbeek, Tom & Kapteyn, Arie, 1989. "Estimation of the error-components model with incomplete panels," Journal of Econometrics, Elsevier, vol. 41(3), pages 341-361, July. [Downloadable!] (restricted)
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This page was last updated on 2009-11-26.

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