Bayesian variable selection in linear regression models with instrumental variables

10 Jan 2019 Sabnis Gautam Atchadé Yves Dovonon Prosper

Many papers on high-dimensional statistics have proposed methods for variable selection and inference in linear regression models by relying explicitly or implicitly on the assumption that all regressors are exogenous. However, applications abound where endogeneity arises from selection biases, omitted variables, measurement errors, unmeasured confounding and many other challenges common to data collection Fan et al. (2014)... (read more)

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