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

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樓主: bradycardia
21#
發(fā)表于 2025-3-25 05:02:29 | 只看該作者
22#
發(fā)表于 2025-3-25 10:59:57 | 只看該作者
stribution economics. Applications include the dynamics of the income distribution over the business cycle or the overlapping-generations model...In an accompanying home page to this book, computer codes to all978-3-540-85685-6
23#
發(fā)表于 2025-3-25 14:18:12 | 只看該作者
Basic Models the algorithms presented in subsequent chapters that use one out of two possible characterizations of a model‘s solution. Second, we develop standard tools in model building and model evaluation used throughout the book..The most basic DGE model is the so called Ramsey model, where a single consume
24#
發(fā)表于 2025-3-25 18:18:36 | 只看該作者
25#
發(fā)表于 2025-3-25 20:05:57 | 只看該作者
Deterministic Extended Path The first section of this chapter employs a non-linear equations solver to obtain the approximate time profile of the optimal capital stock. We then extend this approach to the infinite-horizon deterministic Ramsey model of Section 1.2. At first sight this may seem impossible since this model has a
26#
發(fā)表于 2025-3-26 00:41:15 | 只看該作者
Discrete State Space Methodss case, the value function is a finite dimensional object. For instance, if the state space is one-dimensional and has elements . = {.,., …,.}, the value function is just a vector of . elements where each element gives the value attained by the optimal policy if the initial state of the system is .
27#
發(fā)表于 2025-3-26 07:40:32 | 只看該作者
Parameterized Expectationsilibrium (DGE) model: (1) in terms of a policy function that relates the model‘s decision or control variables to the model‘s state variables or (2) in terms of a system of stochastic difference equations that determines the time paths of the model‘s endogenous variables. The method presented in thi
28#
發(fā)表于 2025-3-26 09:11:02 | 只看該作者
Projection Methodsns and determines the best approximation via Monte-Carlo simulations. In this chapter, we also employ methods from function approximation. Yet, these methods are not limited to functions that determine the agents‘ conditional expectations, nor do they necessarily resort to simulation techniques to f
29#
發(fā)表于 2025-3-26 12:36:56 | 只看該作者
30#
發(fā)表于 2025-3-26 18:08:11 | 只看該作者
Dynamics of the Distribution functionhat this amounts to compute the law of motion for the distribution function .(?, α) of wealth among agents. In the second section, we concentrate on an economy without aggregate uncertainty. The initial distribution is not stationary. For example, this might be the case after a change in policy, e.g
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