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Inference Using Simulated Neural Moments

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  • Michael Creel

Abstract

This paper studies Laplace-type estimators that are based on simulated moments. It shows that confidence intervals using these methods may have coverage which is far from the nominal level. A neural network may be used to reduce the dimension of an initial set of moments to the minimum number that maintains identification. When Laplace-type estimation and inference is based on these neural moments, confidence intervals have statistically correct coverage in most cases studied, with only small departures from correct coverage. The methods are illustrated by an application to a jump diffusion model for returns of the S&P 500 index.

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  • Michael Creel, 2020. "Inference Using Simulated Neural Moments," Working Papers 1182, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:1182
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    Cited by:

    1. Jonathan Chassot & Michael Creel, 2023. "Constructing Efficient Simulated Moments Using Temporal Convolutional Networks," Working Papers 1412, Barcelona School of Economics.

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    More about this item

    Keywords

    neural networks; Laplace type estimators; simulation-based estimation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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