Dynamic General Equilibrium Modelling: Computational Methods by Professor Burkhard Heer, Professor Alfred Maußner (auth.) PDF

By Professor Burkhard Heer, Professor Alfred Maußner (auth.)

ISBN-10: 354022095X

ISBN-13: 9783540220954

ISBN-10: 3540273123

ISBN-13: 9783540273127

Modern company cycle thought and progress concept makes use of stochastic dynamic basic equilibrium types. Many mathematical instruments are had to remedy those types. The ebook offers a variety of tools for computing the dynamics of common equilibrium types. partially I, the representative-agent stochastic development version is solved with the aid of price functionality new release, linear and linear quadratic approximation equipment, parameterised expectancies and projection tools. to be able to observe those equipment, basics from numerical research are reviewed intimately. half II discusses tools for fixing heterogeneous-agent economies. In such economies, the distribution of the person country variables is endogenous. This a part of the booklet additionally serves as an advent to the trendy conception of distribution economics. purposes comprise the dynamics of the source of revenue distribution over the enterprise cycle or the overlapping-generations version. via an accompanying domestic web page to this e-book, machine codes to all purposes may be downloaded.

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28). 2 to the stochastic case, we must include an additional loop over the indices j ∈ {1, 2, . . m} and must replace βv(K ) by the conditional expectation E[v(K , zl )|zj ]. 21) with m–state Markov chain for the productivity shock. Steps: Step 1: Choose a grid G of n equally spaced points over [K, K]: G = [K1 , K2 , . . , Kn ], Ki < Ki+1 ∀i = 1, 2, . . n − 1, K1 = K, Kn = K. Step 2: Initialize the value function as follows: Let U = (uij ). For all j = 1, 2, . . 3 The Stochastic Ramsey Model 47 where Kj∗ solves 1 = β(1 − δ + zj f (Kj∗ )) and i = 1, 2, .

1. Since an in-depth treatment of the analytical framework that underlies stochastic control is beyond the scope of this book we refer the interested reader to Stokey and Lucas (1989). 18 The policy and the value functions are more curved for low values of the capital stock so that the approximation is less accurate in this range. g. Ki = K1 + ζ(i − 1)2 , ζ = (Kn − K1 )/(n − 1)2 or choose the distance between the logarithms of the neighboring grid points, ln Ki − ln Ki+1 , to be constant. However, one can show that neither grid type dominates uniformly across applications.

08 percent. 09 percent. In the first half of the simulation period productivity is either at its intermediate or at its lowest level.

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Dynamic General Equilibrium Modelling: Computational Methods and Applications by Professor Burkhard Heer, Professor Alfred Maußner (auth.)

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