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Markov-Chain Approximations for Life-Cycle Models

Author

Listed:
  • Giulio Fella

    (Queen Mary University of London)

  • Giovanni Gallipoli

    (University of British Columbia)

  • Jutong Pan

    (University of British Columbia)

Abstract

Non-stationary income processes are standard in quantitative life-cycle models, prompted by the observation that within-cohort income inequality increases with age. This paper generalizes Tauchen (1986) and Rouwenhorst's (1995) discretization methods to non-stationary AR(1) processes. We evaluate the performance of both methods in the context of a canonical finite-horizon, income-uctuation problem with a non-stationary income process. We find that the generalized Rouwenhorst's method performs extremely well even with a relatively small number of states.

Suggested Citation

  • Giulio Fella & Giovanni Gallipoli & Jutong Pan, 2017. "Markov-Chain Approximations for Life-Cycle Models," Working Papers 827, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:827
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    References listed on IDEAS

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    Cited by:

    1. Damián Pierri & Gabriel Montes Rojas & Pablo Mira Lambi, 2019. "The Empirical Dimension of Overborrowing," Documentos de trabajo del Instituto Interdisciplinario de Economía Política (IIEP-BAIRES) 2019-45, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política (IIEP-BAIRES).
    2. Mariacristina De Nardi & Giulio Fella & Gonzalo Paz-Pardo, 2020. "Nonlinear Household Earnings Dynamics, Self-Insurance, and Welfare," Journal of the European Economic Association, European Economic Association, vol. 18(2), pages 890-926.

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    More about this item

    Keywords

    Numerical methods; finite state approximations;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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