In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case. The package incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection. It is nonparametric version of 'ivregress' in 'Stata' with IV selection and high dimensional features. The package is based on the paper "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2017) published online in Journal of Business & Economic Statistics
|Author||Qingliang Fan, KongYu He, Wei Zhong|
|Date of publication||2018-02-20 10:11:27 UTC|
|Maintainer||Qingliang Fan <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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