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Computational Solution of Large-Scale Macroeconometric - download pdf or read online

By Giorgio Pauletto

ISBN-10: 1441947787

ISBN-13: 9781441947789

ISBN-10: 1475726317

ISBN-13: 9781475726312

This booklet is the results of my doctoral dissertation examine on the division of Econometrics of the collage of Geneva, Switzerland. This study used to be additionally partly financed by means of the Swiss nationwide technological know-how origin (grants 12- 31072.91 and 12-40300.94). initially, I desire to convey my inner most gratitude to Professor Manfred Gilli, my thesis manager, for his consistent aid and support. i might additionally prefer to thank the president of my jury, Professor Fabrizio Carlevaro, in addition to the opposite contributors of the jury, Professor Andrew Hughes Hallett, Professor Jean-Philippe Vial and Professor Gerhard Wanner. i'm thankful to my colleagues and buddies of the Departement of Econometrics, in particular David Miceli who supplied consistent support and type realizing in the course of the entire levels of my learn. i'd additionally prefer to thank Pascale Mignon for proofreading my textual content and im­ proving my English. eventually, i'm drastically indebted to my mom and dad for his or her kindness and inspire­ ments with out which i'll by no means have accomplished my ambitions. Giorgio Pauletto division of Econometrics, collage of Geneva, Geneva, Switzerland bankruptcy 1 advent the aim of this booklet is to offer the on hand methodologies for the answer of large-scale macroeconometric types. This paintings reports classical answer equipment and introduces more moderen strategies, equivalent to parallel com­ puting and nonstationary iterative algorithms.

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Extra info for Computational Solution of Large-Scale Macroeconometric Models

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A crucial problem is then to minimize the fill-in as the number of operations is proportional to the density of the submatrix to be triangularized. Furthermore, a dense matrix U will result in an expensive back substitution phase. e. 3). A common tradeoff to limit the loss of numerical stability of the sparse Gaussian elimination is to accept a pivot element satisfying the following threshold inequality (k) la kk I ~ u T>

I' ';i . ;. ·~· l :·.. L . i. lh '. : ': ... ;. 2: The quadratic model fj(w) built to determine the minimum w. introduced by diminishing w. A quadratic model of 9 is built-using the information g(O), g(l) and Vg(O)-to find the best approximate w. 2. The minimum value taken by g(w), denoted W, is determined by Vfj(W) =0 :::=} , w= -Vg(O) 2(g(1) - g(O) - Vg(O)) . ,.... 5] so that very small or too large step values are avoided. 25), a further backtrack is performed. e. g(w), a cubic fit is carried out.

A convenient choice is the standard euclidian norm, since it permits an analytical development of the problem. The minimization problem then reads . 20) where the factor 1/2 is added for algebraic convenience. Thus methods for nonlinear least-squares problem, such as Gauss-Newton or Levenberg-Marquardt, can immediately be applied to this framework. Since the system is square and has a solution, we expect to have a zero residual function fat x*. In general it is advisable to take advantage of the structure of F to directly approach the solution of F(x) = O.

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Computational Solution of Large-Scale Macroeconometric Models by Giorgio Pauletto

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