Titlebook: Dynamic General Equilibrium Modeling; Computational Method Burkhard Heer,Alfred Maußner Textbook 20092nd edition Springer-Verlag GmbH Germa

TRUST

思考而得

空洞

Basic Modelstimal time path of consumption; dynamic programming delivers a policy function that relates the agent‘s choice of current consumption to his stock of capital. Both characterizations readily extend to the stochastic version of the infinite-horizon Ramsey model that we introduce in Section 1.3. In Sec

intrigue

Deterministic Extended Pathtions path for its variables. From this path we obtain the solution for the decision variables of the current period. This observation dates back to Fair and Tay l o r (1983) and was applied to the stochastic Ramsey model by Gagnon (1990) from whom we borrowed the label ‘deterministic extended path‘

bonnet

Discrete State Space Methodsization step is simple. We just need to search for the maximal element among n. Yet, the value function of an .-dimensional problem with . different points in each dimension is an array of . different elements and the computation time needed to search this array may be prohibitively high.

PALMY

Parameterized Expectationsarlo techniques to determine their parameters..The PEA has several advantages vis-a-vis both the value function iteration approach and the extended path algorithm. In contrast to the former, it does not suffer as easily from the curse of dimensionality and, therefore, can be applied to models with m

变量

Pde5-Inhibitors

谄媚于人

Dynamics of the Distribution function change in the unemployment compensation system. In the third section, we consider a model with aggregate risk. There are many ways to introduce aggregate risk, but we will focus on a simple case. We distinguish good and bad times which we identify with the boom and recession during the business cyc

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