Statistical inference in micro-simulation models: incorporating external information

In practical applications of micro-simulation models (MSMs), very little is usually known about the properties of the simulated values. This paper argues that we need to apply the same rigorous standards for inference in micro-simulation work as in scientific work generally. If not, then MSMs will l...

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Bibliographic Details
Published inMathematics and computers in simulation Vol. 59; no. 1; pp. 255 - 265
Main Author Anders Klevmarken, N.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Amsterdam Elsevier B.V 10.05.2002
Elsevier
Subjects
Alignment
Applied sciences
Computer science; control theory; systems
Constrained estimation
Control system analysis
Control theory. Systems
Exact sciences and technology
Micro-simulation
Simulation estimation
Validation
Validation
Alignment
Constrained estimation
Simulation estimation
Micro-simulation
Parameter estimation
Computer simulation
Estimation
Behavioral model
Inference
Statistical estimation
Dynamical system
Simulation
Simulation model
Dynamic model
Microsimulation model
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ISSN0378-4754
1872-7166
DOI10.1016/S0378-4754(01)00413-X

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Summary:In practical applications of micro-simulation models (MSMs), very little is usually known about the properties of the simulated values. This paper argues that we need to apply the same rigorous standards for inference in micro-simulation work as in scientific work generally. If not, then MSMs will loose in credibility. Differences between inference in static and dynamic models are noted and then the paper focuses on the estimation of behavioral parameters. There are four themes: calibration viewed as estimation subject to external constraints, piece meal versus system-wide estimation, simulation-based estimation and validation.
ISSN:0378-4754
1872-7166
DOI:10.1016/S0378-4754(01)00413-X