Empirical Calibration of Simulation Models
AbstractSimulations have become a common tool to study the implications of theoretical models. In addition, computational approaches are frequently used in statistics to adapt models to reality. We aim to merge these approaches. To obtain robust results with significant validity from simulations, empirical data have to be extensively used. We first discuss how inference in economic contexts can be made and then describe our proposed method. It makes extensive use of empirical knowledge for the development of a simulation model whose implications are then examined in the light of empirical data in a Bayesian-like approach. This allows all systems that can be described by the simulation model to be classified into subsets of models. This makes it possible to establish different subsets of models that describe certain realities and to study the characteristics of and causal relationships in these subsets
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 89.
Date of creation: 11 Aug 2004
Date of revision:
Simulations; Empirics; Methodology;
Other versions of this item:
- Claudia Werker & Thomas Brenner, 2004. "Empirical Calibration of Simulation Models," Papers on Economics and Evolution 2004-10, Max Planck Institute of Economics, Evolutionary Economics Group.
- Werker, C. & Brenner, T., 2004. "Empirical calibration of simulation models," Eindhoven Center for Innovation Studies (ECIS) working paper series 04.13, Eindhoven Center for Innovation Studies (ECIS).
- B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
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