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Matlab code for "Numerical solution of dynamic equilibrium models under Poisson uncertainty"

Author

Listed:
  • Olaf Posch

    (Hamburg University)

  • Timo Trimborn

    (University of Gottingen)

Programming Language

Matlab

Abstract

We propose a simple and powerful numerical algorithm to compute the transition process in continuous-time dynamic equilibrium models with rare events. In this paper we transform the dynamic system of stochastic differential equations into a system of functional differential equations of the retarded type. We apply the Waveform Relaxation algorithm, i.e., we provide a guess of the policy function and solve the resulting system of (deterministic) ordinary differential equations by standard techniques. For parametric restrictions, analytical solutions to the stochastic growth model and a novel solution to Lucas' endogenous growth model under Poisson uncertainty are used to compute the exact numerical error. We show how (potential) catastrophic events such as rare natural disasters substantially affect the economic decisions of households.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Olaf Posch & Timo Trimborn, 2013. "Matlab code for "Numerical solution of dynamic equilibrium models under Poisson uncertainty"," QM&RBC Codes 199, Quantitative Macroeconomics & Real Business Cycles.
  • Handle: RePEc:dge:qmrbcd:199
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    File URL: https://dge.repec.org/codes/trimborn/Posch_Trimborn_2013_SDE.zip
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    Keywords

    Matlab;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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