Journal of Computational Physics
Recent developments and trends in global optimization
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Computational Economics
Estimation of agent-based models: the case of an asymmetric herding model
Computational Economics
Computational Statistics & Data Analysis
Validating and Calibrating Agent-Based Models: A Case Study
Computational Economics
Empirical Validation in Agent-based Models: Introduction to the Special Issue
Computational Economics
Discontinuities in indirect estimation: An application to EAR models
Computational Statistics & Data Analysis
Optimal aggregation of linear time series models
Computational Statistics & Data Analysis
Preface: Second Special issue on Computational Econometrics
Computational Statistics & Data Analysis
Estimation of a Structural Stochastic Volatility Model of Asset Pricing
Computational Economics
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A continuous global optimization heuristic for a stochastic approximation of an objective function, which itself is not globally convex, is introduced. The objective function arises from the simulation based indirect estimation of the parameters of agent based models of financial markets. The function is continuous in the variables but non-differentiable. Due to Monte Carlo variance, only a stochastic approximation of the objective function is available. The algorithm combines features of the Nelder-Mead simplex algorithm with those of a local search heuristic called threshold accepting. The Monte Carlo variance of the simulation procedure is also explicitly taken into account. We present details of the algorithm and some results of the estimation of the parameters for a specific agent based model of the DM/US-$ foreign exchange market.