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Discretizing earnings dynamics: implications of Gaussian-mixture shocks for life-cycle models

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  • Robert Kirkby

    (Victoria University of Wellington)

Abstract

The standard AR(1) process with gaussian innovations, commonly used in life-cycle models of earnings dynamics, has proven insufficient. Recent research highlights the age-dependent and non-Gaussian nature of earnings changes. Empirical studies show that an AR(1) process with Gaussian-mixture innovations, along with age-dependent parameters, provides a better fit to this evidence. However, implementing such a model in a life-cycle framework requires discretization. To address this, we extend the Tanaka–Toda method and evaluate its performance. We then apply this method to a standard life-cycle model, discretizing various earnings processes to assess the impact of more realistic earnings dynamics compared to the traditional AR(1) process with gaussian innovations. We find that non-gaussian innovations and non-employment shocks are important to both annual and lifetime inequality. They also improve the performance of life-cycle models on consumption inequality and consumption insurance. Given their important impact on model results and empirical realism, the use of gaussian-mixtures provide an accessible improvement to life-cycle models. We provide Matlab code for the implementation of this discretization method.

Suggested Citation

  • Robert Kirkby, 2025. "Discretizing earnings dynamics: implications of Gaussian-mixture shocks for life-cycle models," The Japanese Economic Review, Springer, vol. 76(2), pages 493-519, April.
  • Handle: RePEc:spr:jecrev:v:76:y:2025:i:2:d:10.1007_s42973-025-00196-7
    DOI: 10.1007/s42973-025-00196-7
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    More about this item

    Keywords

    Numerical methods; Quadrature; Gaussian mixture; Life-cycle model;
    All these keywords.

    JEL classification:

    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity

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