Empirical Calibration of Simulation Models
This paper discusses how the results of simulation models can be made more reliable and the method of simulating therefore more widely applicable. We suggested to calibrate simulation models empirically and developed a methodology based on Critical Realism in order to so. We suggested combining the procedures of two strands of literature: the empirical underpinning of the assumptions (like in microsimulations) and the empirical check of the implications (like in Bayesian inference). Both these strands of literature are mainly concerned with predicting future developments. We, instead, aim to infer statements about causal relations and characteristics of a set of systems or dynamics, such as, e.g., the development of an industry, that have a general validity for this set of systems or dynamics. In other words, instead of deriving probabilistic predictions of the future and statements of the current situation and dynamics of one single system we developed a methodology to gain general statements about the features of systems and dynamics.
|Date of creation:||May 2004|
|Contact details of provider:|| Postal: Deutschhausstrasse 10, 35032 Marburg|
Web page: http://www.uni-marburg.de/fb19/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Franco Malerba & Luigi Orsenigo, 2002. "Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model," Industrial and Corporate Change, Oxford University Press, vol. 11(4), pages 667-703, August.
- Finn E. Kydland & Edward C. Prescott, 1996.
"The Computational Experiment: An Econometric Tool,"
Journal of Economic Perspectives,
American Economic Association, vol. 10(1), pages 69-85, Winter.
- Finn E. Kydland & Edward C. Prescott, 1994. "The computational experiment: an econometric tool," Working Paper 9420, Federal Reserve Bank of Cleveland.
- Finn E. Kydland & Edward C. Prescott, 1994. "The computational experiment: an econometric tool," Staff Report 178, Federal Reserve Bank of Minneapolis.
- Johann Peter Murmann & Thomas Brenner, 2003. "The Use of Simulations in Developing Robust Knowledge about Causal Processes: Methodological Considerations and an Application to Industrial Evolution," Computing in Economics and Finance 2003 66, Society for Computational Economics.
- Schwerin, Joachim & Werker, Claudia, 2003. "Learning innovation policy based on historical experience," Structural Change and Economic Dynamics, Elsevier, vol. 14(4), pages 385-404, December.
- Machlup, Fritz, 1978. "Methodology of Economics and Other Social Sciences," Elsevier Monographs, Elsevier, edition 1, number 9780124645509 edited by Shell, Karl.
- Dominique Foray & Robin Cowan, 2002. "Evolutionary economics and the counterfactual threat: on the nature and role of counterfactual history as an empirical tool in economics," Journal of Evolutionary Economics, Springer, vol. 12(5), pages 539-562.
- Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press, vol. 8(1), pages 3-40, March. Full references (including those not matched with items on IDEAS)