A Methodological Note on the Estimation of Programming Models

The paper introduces a general methodological approach for the estimation of constrained optimisation models in agricultural supply analysis. It is based on optimality conditions of the desired programming model and shows a conceptual advantage compared to Positive Mathematical Programming in the context of well posed estimation problems. Moreover, it closes the empirical and methodological gap between programming models and duality based functional models with explicit allocation of fixed factors. Monte Carlo simulations are performed with a maximum entropy estimator to evaluate the functionality of the approach as well as the impact of empirically relevant prior information in small sample situations.

[1]  George G. Judge,et al.  Econometric foundations , 2000 .

[2]  Bob Chirinko,et al.  Business Fixed Investment Spending: Modeling Strategies, Empirical Results, and Policy Implications , 1993 .

[3]  Paolo Sckokai,et al.  Modelling the CAP arable crop regime in Italy : degree of decoupling and impact of Agenda 2000 , 1999, Cahiers d'Economie et sociologie rurales.

[4]  Douglas J. Miller,et al.  Estimation of Multi-Output Production Functions with Incomplete Data: A Generalized Maximum Entropy Approach , 1998 .

[5]  Bruno Henry de Frahan,et al.  Analysing the effects of Agenda 2000 using a CES Calibrated model of Belgian agriculture , 2001 .

[6]  Richard E. Howitt,et al.  Positive Mathematical Programming , 1995 .

[7]  Amos Golan,et al.  Maximum entropy econometrics , 1996 .

[8]  P. Preckel.  Least Squares and Entropy: A Penalty Function Perspective , 2001 .

[9]  Douglas J. Miller,et al.  Recovering Output‐Specific Inputs from Aggregate Input Data: A Generalized Cross‐Entropy Approach , 1998 .

[10]  Xiaobo Zhang,et al.  Estimating Crop‐Specific Production Technologies in Chinese Agriculture: A Generalized Maximum Entropy Approach , 2001 .

[11]  Paul V. Preckel.  An Analysis of Ill-Posed Production Problems Using Maximum Entropy: Comment , 2002 .

[12]  K. M. Ortner.  Agricultural sector modelling and policy information systems - Report of the 65th EAAE seminar in Bonn in March 2000. , 2000 .

[13]  Richard E. Howitt,et al.  An Analysis of Ill‐Posed Production Problems Using Maximum Entropy , 1998 .

[14]  Alfons Oude Lansink.  Generalised maximum entropy estimation and heterogeneous technologies , 1999 .

[15]  Hervé Guyomard,et al.  Estimating crop supply response in the presence of farm programmes: application to the CAP , 1996 .

[16]  Richard E. Howitt,et al.  A CALIBRATION METHOD FOR AGRICULTURAL ECONOMIC PRODUCTION MODELS , 1995 .

[17]  Richard Howitt,et al.  The Canadian Regional Agriculture Model Structure, Operation and Development , 1992 .

[18]  John Helming,et al.  Assessing the consequences of environmental policy scenarios in Flemish agriculture , 2001 .

[19]  A. Barkaoui,et al.  Positive mathematical programming and cereals and oilseeds supply within EU under Agenda 2000 , 1999 .

[20]  George G. Judge,et al.  A Maximum Entropy Approach to Recovering Information from Multinomial Response Data , 1996 .

[21]  Robert Chambers,et al.  Estimating Multioutput Technologies , 1989 .

[22]  W. Henrichsmeyer,et al.  Agricultural Sector Modelling , 1995 .

[23]  Thomas Heckelei,et al.  Positive Mathematical Programming with Multiple Data Points: A Cross-Sectional Estimation Procedure , 2000 .

[24]  R. E. Howitt,et al.  Modeling production and resource interrelationships among California crops in response to the 1985 Food Security Act , 1986 .