Prudence and Robustness as Explanations for Precautionary Savings; an Evaluation
AbstractThis paper evaluates approximation methods to make manageable the numerical solution of overlapping generation models with aggregate risk. The paper starts with a model in which households maximize expected utility over their life cycle. Instantaneous utility is characterized by constant relative risk aversion. Prudence, a characteristic of the utility function, leads to precautionary saving. The first-order conditions include expectations. One source of uncertainty is not prohibitive for numerical integration of the expectation term. Because of its accuracy numerical integration results are used as a bench mark. Taylor series approximations can lead to the same results dependent on the linearization point. A linear quadratic approximation of the household model is evaluated subsequently. Alternatively, precautionary saving effects can be the result of robust decision making. This approach leads to linear policy functions and gives a rather good approximation of the bench mark model, although not as good as the Taylor series approximation.
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Bibliographic InfoPaper provided by CPB Netherlands Bureau for Economic Policy Analysis in its series CPB Memorandum with number 196.
Date of creation: Apr 2008
Date of revision:
Find related papers by JEL classification:
- E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-10-13 (All new papers)
- NEP-DGE-2008-10-13 (Dynamic General Equilibrium)
- NEP-MAC-2008-10-13 (Macroeconomics)
- NEP-UPT-2008-10-13 (Utility Models & Prospect Theory)
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