Computational Solution of Large-Scale Macroeconometric by Giorgio Pauletto

By Giorgio Pauletto

This e-book is the results of my doctoral dissertation learn on the division of Econometrics of the college of Geneva, Switzerland. This examine was once additionally in part financed via the Swiss nationwide technology starting place (grants 12- 31072.91 and 12-40300.94). at the start, I desire to exhibit my inner most gratitude to Professor Manfred Gilli, my thesis manager, for his consistent aid and aid. i'd additionally wish to thank the president of my jury, Professor Fabrizio Carlevaro, in addition to the opposite participants 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, specially David Miceli who supplied consistent aid and sort figuring out in the course of all of the levels of my study. i might additionally wish to thank Pascale Mignon for proofreading my textual content and im­ proving my English. ultimately, i'm significantly indebted to my mom and dad for his or her kindness and inspire­ ments with out which i'll by no means have completed my ambitions. Giorgio Pauletto division of Econometrics, collage of Geneva, Geneva, Switzerland bankruptcy 1 advent the aim of this e-book is to provide the on hand methodologies for the answer of large-scale macroeconometric versions. This paintings experiences classical resolution tools and introduces newer recommendations, resembling parallel com­ puting and nonstationary iterative algorithms.

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Given F : IRn -+ IRn continuously differentiable and a starting point Evaluate A (0) by V' F(x(O» or J(O) for k = 0,1,2, ... until convergence Solve for s(k) A(k) s(k) = -F(x(k» x(k+l) = X(k) + S(k) y(k) = F(x(k+ 1» _ F(x(k» = A(k) + «y(k) _ A(k)s(k» A(k+l) end s(k)')/(s(k)s(k),) x(O) E IRn 42 A Review of Solution Techniques Broyden's method may generate sequences of matrices {A(k)h=o,l, ... which do not converge to the Jacobian matrix \7 F(x*), even though the method produces a sequence {x(k) h=O,l, ...

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. However, in some circumstances, resorting to the minimization of f(x) constitutes an interesting alternative.

Such an update necessitates O(n 2 ) operations, therefore reducing the original O(n 3 ) cost of a complete refactorization. Practically, the QR factorization update is easier to implement than the LV update, see Gill et al. [47, pp. 125-150]. For sparse systems, however, the advantage of the updating process vanishes. A software reference for Broyden's method is MINPACK by More, Garbow and Hillstrom available on NETLIB. 4 can be extended to nonlinear equations. 4 in terms of obtaining x~k+1) as the solution of the j-th equation with the other (n - 1) variables held fixed, we may immediately apply the same idea to the nonlinear case.

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