Solving Linear Rational Expectations Models
Computational Economics
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Applications of high dimensionalmodel representations to computer vision
WSEAS Transactions on Mathematics
Applications of high dimensional model representations to computer vision
MAASE'09 Proceedings of the 2nd WSEAS international conference on Multivariate analysis and its application in science and engineering
Applications of flexibly initialized high dimensional model representation in computer vision
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
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We present computational tools to analyse some key properties of DSGE models and address the following questions: (i) Which is the domain of structural coefficients assuring the stability and determinacy of a DSGE model? (ii) Which parameters mostly drive the fit of, e.g., GDP and which the fit of inflation? Is there any conflict between the optimal fit of one observed series versus another one? (iii) How to represent in a direct, albeit approximated, form the relationship between structural parameters and the reduced form of a rational expectations model? Global sensitivity analysis (GSA) techniques are used to answer these questions. We will discuss two classes of methods: Monte Carlo filtering (MCF) techniques and functional/variance decomposition techniques. These tools can make the model properties more transparent; helping the analyst to identify critical elements in the specification and, if necessary, guiding her to revise the model; supporting calibration and estimation procedures and interpreting estimation results. Applications to small DSGE models will complete the description of the methodologies.