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Information theoretic approach to high dimensional multiplicative models: Stochastic discount factor and treatment effect

Author

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  • Taisuke Otsu
  • Chen Qiu
Abstract
This paper is concerned with estimation of functionals of a latent weight function that satisfies possibly high dimensional multiplicative moment conditions. Main examples are missing data problems, treatment effects, and functionals of the stochastic discount factor in asset pricing. We propose to estimate the latent weight function by an information theoretic approach combined with the 1-penalization technique to deal with high dimensional moment conditions under sparsity. We derive asymptotic properties of the proposed estimator, and illustrate the proposed method by a theoretical example on treatment effect analysis and empirical example on the stochastic discount factor.

Suggested Citation

  • Taisuke Otsu & Chen Qiu, 2018. "Information theoretic approach to high dimensional multiplicative models: Stochastic discount factor and treatment effect," STICERD - Econometrics Paper Series 595, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:595
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    References listed on IDEAS

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    Cited by:

    1. Karun Adusumilli & Taisuke Otsu & Chen Qiu, 2020. "Reweighted nonparametric likelihood inference for linear functionals," STICERD - Econometrics Paper Series 614, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

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    More about this item

    Keywords

    Stochastic discount factor; Treatment effect; Information theory; High dimension;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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