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GMM with Multiple Missing Variables

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  • Saraswata Chaudhuri
  • David K. Guilkey

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  • Saraswata Chaudhuri & David K. Guilkey, 2016. "GMM with Multiple Missing Variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 678-706, June.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:4:p:678-706
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    Cited by:

    1. Saraswata Chaudhuriy & David T. Frazierz & Eric Renault, 2016. "Indirect Inference with Endogenously Missing Exogenous Variables," CIRANO Working Papers 2016s-15, CIRANO.
    2. Cui, Li-E & Zhao, Puying & Tang, Niansheng, 2022. "Generalized empirical likelihood for nonsmooth estimating equations with missing data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    3. Bang, Minji & Gao, Wayne Yuan & Postlewaite, Andrew & Sieg, Holger, 2023. "Using monotonicity restrictions to identify models with partially latent covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 892-921.
    4. Rocha, Leonardo Andrade & Silva, Napiê Galvê Araújo & Almeida, Carlo Alano Soares de & Oliveira, Denison Murilo de & Fernandes, Kaio César, 2020. "Growth and heterogeneity of human capital: effects of the expansion of higher education on the income increase in Brazilian municipalities," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    5. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian Instrumental Variables Estimation for Nonignorable Missing Instruments," Discussion Paper Series DP2020-06, Research Institute for Economics & Business Administration, Kobe University.
    6. Chaudhuri, Saraswata & Frazier, David T. & Renault, Eric, 2018. "Indirect Inference with endogenously missing exogenous variables," Journal of Econometrics, Elsevier, vol. 205(1), pages 55-75.

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