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Parameterising a detailed dynamic programming model of savings and labour supply using cross-sectional data

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  • Justin W. van de Ven

    (NIESR, 2 Dean Trench Street, London SW1P 3HE, UK - MIAESR, The University of Melbourne, Australia)

Abstract

Dynamic programming methods are now commonly used to describe behaviour in contexts where uncertainty is likely to have an important bearing on decision making. Using a publicly available structural dynamic microsimulation model, LINDA, this paper provides new insights into how unobservable preference parameters ? particularly those associated with risk aversion ? can be coherently identified on broad-based moments of decision making observed for a population cross-section. Preference parameters identified on UK data are found to be in-line with those reported in the wider econometric literature

Suggested Citation

  • Justin W. van de Ven, 2017. "Parameterising a detailed dynamic programming model of savings and labour supply using cross-sectional data," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 135-166.
    • Handle: RePEc:ijm:journl:v10:y:2017:i:1:p:135-166
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    More about this item

    Keywords

    Dynamic Programming; Savings; Labor Supply; Empirical Identification;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household

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