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A nonparametric approach to solving a simple one-sector stochastic growth model

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  • Shaw, Philip

Abstract

In this paper we present a nonparametric approach to solving a simple one-sector stochastic growth model. A distinct advantage of our approach is that it does not require placing restrictions on the generally unknown conditional expectations functions. Our method is shown to be accurate and computationally stable when compared to the standard Parameterized Expectations Approach (PEA) and the traditional linear approximation. We demonstrate this using a simple stochastic general equilibrium model with a known solution.

Suggested Citation

  • Shaw, Philip, 2014. "A nonparametric approach to solving a simple one-sector stochastic growth model," Economics Letters, Elsevier, vol. 125(3), pages 447-450.
    • Handle: RePEc:eee:ecolet:v:125:y:2014:i:3:p:447-450
    DOI: 10.1016/j.econlet.2014.10.011
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    References listed on IDEAS

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    1. Duffy, John & McNelis, Paul D., 2001. "Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 25(9), pages 1273-1303, September.
    2. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models," Quantitative Economics, Econometric Society, vol. 2(2), pages 173-210, July.
    3. Andrei Jirnyi & Vadym Lepetyuk, 2011. "A reinforcement learning approach to solving incomplete market models with aggregate uncertainty," Working Papers. Serie AD 2011-21, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    4. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
    5. Maliar, Lilia & Maliar, Serguei, 2003. "Parameterized Expectations Algorithm and the Moving Bounds," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 88-92, January.
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    More about this item

    Keywords

    Nonparametric econometrics; Computational methods; Parameterized expectations algorithm;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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