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Estimation and Inference by Stochastic Optimization: Three Examples

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  • Jean-Jacques Forneron
  • Serena Ng

Abstract

This paper illustrates two algorithms designed in Forneron and Ng (2020): the resampled Newton-Raphson (rNR) and resampled quasi-Newton (rQN) algorithms, which speed up estimation and bootstrap inference for structural models. An empirical application to BLP shows that computation time decreases from nearly five hours with the standard bootstrap to just over one hour with rNR and to only 15 minutes using rQN. A first Monte Carlo exercise illustrates the accuracy of the method for estimation and inference in a probit IV regression. A second exercise additionally illustrates statistical efficiency gains relative to standard estimation for simulation-based estimation using a dynamic panel regression example.

Suggested Citation

  • Jean-Jacques Forneron & Serena Ng, 2021. "Estimation and Inference by Stochastic Optimization: Three Examples," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 626-630, May.
    • Handle: RePEc:aea:apandp:v:111:y:2021:p:626-30
    DOI: 10.1257/pandp.20211038
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    References listed on IDEAS

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    1. Kline Patrick & Santos Andres, 2012. "A Score Based Approach to Wild Bootstrap Inference," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 23-41, August.
    2. Brunner, Daniel & Heiss, Florian & Romahn, André & Weiser, Constantin, 2017. "Reliable estimation of random coefficient logit demand models," DICE Discussion Papers 267, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
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    Cited by:

    1. Forneron, Jean-Jacques, 2024. "Estimation and inference by stochastic optimization," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2022. "Fast Inference for Quantile Regression with Tens of Millions of Observations," Papers 2209.14502, arXiv.org, revised Oct 2023.

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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