Decision support systems for large dam planning and operation in Africa

Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects

A BAYESIAN ALTERNATIVE TO GENERALIZED CROSS ENTROPY SOLUTIONS FOR UNDERDETERMINED ECONOMETRIC MODELS

This paper presents a Bayesian alternative to Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) methods for deriving solutions to econometric models represented by underdetermined systems of equations. For certain types of econometric model specifications, the Bayesian approach provides fully equivalent results to GME-GCE techniques. However, in its general form, the proposed Bayesian methodology allows a more direct and straightforwardly interpretable formulation of available prior information and can reduce significantly the computational effort involved in finding solutions. The technique can be adapted to provide solutions in situations characterized by either informative or uninformative prior information.Underdetermined Equation Systems, Maximum Entropy, Bayesian Priors, Structural Estimation, Calibration, Research Methods/ Statistical Methods, C11, C13, C51,

Regional crop supply behaviour in the EU

The objective of this paper is to present an evolution of PMP model suitable to estimate the revenue function and to provide price elasticity due to the variation of subsidies at farm level, especially if they are decoupled. This problem arises when individual data of farm households in a given region, coming from FADN, are used for implement PMP models finalized to policy analysis. This paper presents the theoretical background of the proposed innovations and empirical evidence on the basis of a sample of farms included in FADN database in Italy.Bayesian estimation, errors-in-variables, PMP, Crop Production/Industries,

Estimating input allocation for farm supply models

When building an economic model for supply analysis the aim is to model a decision making process of one or more agents which fits the observed practice as good as possible. Hereby the modeller is often confronted with incomplete information about the production process; particular crop specific input data are rarely available. The problem of defining activity related technology inputs coefficients is not new. A good deal of literature comes from the mathematical programming perspective, where input coefficients were estimated using a standard linear regression function to fully represent the mathematical program. However this approach is a pure technical device and may result in an inconsistent model. The author of the paper wants to investigate whether it is possible, employing proper estimation techniques, to simultaneously estimate all unknown coefficients of a mathematical farm supply model. This includes the estimation of parameters of the non linear cost function, used to calibrate and catch the simulation behaviour and the crop specific input coefficients. It is shown that a simultaneous estimation of all parameters improves the goodness of fit of the estimated parameters and that such an approach is technically feasible.farm supply model, input allocation, entropy, HDP, Research Methods/ Statistical Methods,