By D. Royer, G. Ritschard (auth.), J. P. Ancot (eds.)
Understanding the constitution of a big econometric version is just like the artwork of winetasting or just like the paintings of taking part in a musical software. the standard of a wine effects from a fancy mix of varied parts corresponding to its color which could be transparent and crystalline, its odor which are decomposed right into a common aroma and various specific features, roughly power reckoning on the sort and the age of the wine, its flavor, after all, which back is a posh approach whose equilibrium and beauty rely on the full set of components: alcohol, tannin, glycerine, sugar, acidity . . . equally, a clarinetist's musicianship depends upon the standard of his software, on his embouchure, fingering, tonguing and articu lation thoughts, on his experience for rhythm, phasing and tone color. besides the fact that, the attraction produced through a Romanee-Conti or via a super functionality of Brahm's F minor sonata for clarinet and piano arises from a technique that's even as time a lot less complicated and masses extra advanced than the easy juxtaposition of person causal kinfolk. lately econometricians and macro-economists were challenged by way of the matter of protecting abreast with an ever expanding variety of an increasing number of complicated huge econometric types. the need of constructing systematic analytical instruments to review the customarily implicit and hidden constitution of those types has turn into extra evident.
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Additional resources for Analysing the Structure of Econometric Models
The reduction of G gives the graph G* whose vertices yj, Yj ... C. of G. - Strict causality CS: ++ (yj, yj) is an arc of G*. C. C. C. of G. ) and of the relation Cs which orders them strictly by levels. Simon (1957) has already underlined the importance of the 'causal ordering' obtained and he related it to requirements of the human mind for clustering knowledge or ideas and for organising them in hierarchies or trees; here, we aggregate the 'strongly connected' variables in blocks and we organise these blocks linearly in such a manner that their relationships become evident.
Figure 2 shows a graphical representation of a smallest minimal graph associated with the interdependent block of the model. The arcs drawn in fat lines represent the basic arcs. The depth first search algorithm identifies only one smallest minimal graph. In order to find all smallest minimal graphs a procedure involving combination calculus is needed. , in the case of economic models smallest minimal graphs are not very different from each other. Therefore the conclusions of our analysis would not be deeply modified by the choice of another smallest minimal graph.
Nevertheless, if these results are robust (because not at variance with the first step results), they are insufficient; they do not give real insight in the macroeconomic structure apart from illustrating the importance of the interdependence in the macroeconomic models. This step must therefore be extended by the decomposition of the large block and the computation of a finer hierarchy between the significant variables. 4_ THIRD STEP OF THE READING: BUILDING AN APPROXIMATE HIERARCHICAL CAUSALITY RELATION The circular causality Cc which we have introduced above appears as a contradictory concept since the causality implies an asymmetry and the circularity a symmetry.
Analysing the Structure of Econometric Models by D. Royer, G. Ritschard (auth.), J. P. Ancot (eds.)