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Econometric reviews. 2014;33(1-4):10.1080/07474938.2013.807150. doi: 10.1080/07474938.2013.807150 Q40.82024

Two-Stage Bayesian Model Averaging in Endogenous Variable Models

内生变量模型中的两阶段贝叶斯模型平均方法 翻译改进

Alex Lenkoski  1, Theo S Eicher, Adrian E Raftery

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  • 1 Institute for Applied Mathematics, Heidelberg University.
  • DOI: 10.1080/07474938.2013.807150 PMID: 24223471

    摘要 Ai翻译

    Economic modeling in the presence of endogeneity is subject to model uncertainty at both the instrument and covariate level. We propose a Two-Stage Bayesian Model Averaging (2SBMA) methodology that extends the Two-Stage Least Squares (2SLS) estimator. By constructing a Two-Stage Unit Information Prior in the endogenous variable model, we are able to efficiently combine established methods for addressing model uncertainty in regression models with the classic technique of 2SLS. To assess the validity of instruments in the 2SBMA context, we develop Bayesian tests of the identification restriction that are based on model averaged posterior predictive p-values. A simulation study showed that 2SBMA has the ability to recover structure in both the instrument and covariate set, and substantially improves the sharpness of resulting coefficient estimates in comparison to 2SLS using the full specification in an automatic fashion. Due to the increased parsimony of the 2SBMA estimate, the Bayesian Sargan test had a power of 50 percent in detecting a violation of the exogeneity assumption, while the method based on 2SLS using the full specification had negligible power. We apply our approach to the problem of development accounting, and find support not only for institutions, but also for geography and integration as development determinants, once both model uncertainty and endogeneity have been jointly addressed.

    Keywords: Bayesian Model Averaging; Endogeneity; Instrumental variables; Posterior Predictive P-Values.

    Keywords:endogenous variables

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    期刊名:Econometric reviews

    缩写:ECONOMET REV

    ISSN:0747-4938

    e-ISSN:1532-4168

    IF/分区:0.8/Q4

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