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Empirical Analysis of Household Savings Decisions in Context of Uncertainty: A Cross-Sectional Approach

Author

Listed:
  • Justin van de Ven

    (National Institute of Economic and Social Research, London; Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Paolo Lucchino

    (National Institute of Economic and Social Research, London)

Abstract

Most empirical studies of savings behaviour that explicitly take account of the influence of uncertainty consider for identification data that describe the evolution of circumstances observed during an appreciable period of the life-course. Here we report results obtained for a dynamic programming model that has been adapted to permit identification of preference parameters using data observed at a point in time for a given population cross-section. We discuss the advantages of this approach, and our empirical results demonstrate its feasibility in context of contemporary computing technology.

Suggested Citation

  • Justin van de Ven & Paolo Lucchino, 2013. "Empirical Analysis of Household Savings Decisions in Context of Uncertainty: A Cross-Sectional Approach," Melbourne Institute Working Paper Series wp2013n21, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2013n21
    as

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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2013n21.pdf
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    References listed on IDEAS

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    Cited by:

    1. Berk Yavuzoglu, 2018. "Labor Force Attachment Beyond Normal Retirement Age," Working Papers 1803, Nazarbayev University, Department of Economics, revised Nov 2018.
    2. Justin van de Ven, 2013. "The Influence of Decision Costs on Investments in Indivudual Savings Accounts," Melbourne Institute Working Paper Series wp2013n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.

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    More about this item

    Keywords

    Dynamic programming; savings; labor supply;
    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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