The development of automated (computer-based) calibration methods has focused mainly on the selection of a singleobjective measure of the distance between the model-simulated output and the data and the selection of an automatic optimization algorithm to search for the parameter values which minimize that distance. However, practical experience with model calibration suggests that no single-obj...

A Model as a Simplification of Reality There is no single model of an economic system. In general, a model is a simplified approximation of reality, and there must surely be many such approximations. Therefore, we have large and small models, real and nominal models, sector and aggregative models, dynamic and static models, long-and short-run models, and so on. The model being used at any one t...

We describe a new variable selection procedure for categorical responses where the candidate models are all probit regression models. The procedure uses objective intrinsic priors for the model parameters, which do not depend on tuning parameters, and ranks the models for the different subsets of covariates according to their model posterior probabilities. When the number of covariates is moder...

Abstract Objective priors, especially reference priors, have been studied extensively for spatial data in the last decade. In this paper, we study objective priors for a CAR model. In particular, the properties of the reference prior and the corresponding posterior are studied. Furthermore, we show that the frequentist coverage probabilities of posterior credible intervals depend only on the sp...

Identi cation of model parameters can be viewed as a problem with multiple objectives and constraints derived from empirical data (dynamic and steadystate), physical models and belief, empirical and qualitative belief, desired model properties etc. A fairly general approach to multi-objective system identi cation based on constrained optimization is suggested, and here we formalize the method f...

Computational models are of increasing complexity and their behavior may in particular emerge from the interaction of different parts. Studying such models becomes then more and more difficult and there is a need for methods and tools supporting this process. Multi-objective evolutionary algorithms generate a set of trade-off solutions instead of a single optimal solution. The availability of a...