I. Arribas, K. Louhichi, Ángel Perni, J. Vila, S. Gomez-y-Paloma
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Modelling agricultural risk in a large scale positive mathematical programming model
Mathematical programming has been extensively used to account for risk in farmers' decision making. The recent development of the positive mathematical programming (PMP) has renewed the need to incorporate risk in a more robust and flexible way. Most of the existing PMP-risk models have been tested at farm-type level and for a very limited sample of farms. This paper presents and tests a novel methodology for modelling risk at individual farm level in a large scale model, called individual farm model for common agricultural policy analysis (IFM-CAP). Results show a clear trade-off between including and excluding the risk specification. Albeit both alternatives provide very close estimates, simulation results shows that the explicit inclusion of risk in the model allows isolating risk effects on farmer behaviour. However, this specification increases three times the computation time required for estimation.
期刊介绍:
IJCEE explores the intersection of economics, econometrics and computation. It investigates the application of recent computational techniques to all branches of economic modelling, both theoretical and empirical. IJCEE aims at an international and multidisciplinary standing, promoting rigorous quantitative examination of relevant economic issues and policy analyses. The journal''s research areas include computational economic modelling, computational econometrics and statistics and simulation methods. It is an internationally competitive, peer-reviewed journal dedicated to stimulating discussion at the forefront of economic and econometric research. Topics covered include: -Computational Economics: Computational techniques applied to economic problems and policies, Agent-based modelling, Control and game theory, General equilibrium models, Optimisation methods, Economic dynamics, Software development and implementation, -Econometrics: Applied micro and macro econometrics, Monte Carlo simulation, Robustness and sensitivity analysis, Bayesian econometrics, Time series analysis and forecasting techniques, Operational research methods with applications to economics, Software development and implementation.