REndo: Fitting Linear Models with Endogenous Regressors using Latent...

In REndo: Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables

Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables

Description

Fits linear models with endogenous regressor using latent instrumental variable approaches.

The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroskedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.

The main functions to estimate models are:

• latentIV()the latent instrumental variables method of Ebbes et al. (2005)

• copulaCorrection()copula correction method proposed by Paek and Gupta (2012)

• hetErrorsIV()heteroskedastic errors approach proposed by Lewbel(2012)

• higherMomentsIV()higher moments method proposed by Lewbel (1997)

• multilevelIV()multilevel GMM method proposed by Kim and Frees (2007)

Differences between current (2.0.0) and previous version of REndo

Note that with version 2.0.0 sweeping changes were which greatly improve functionality but break backwards compatibility. Various bugs were fixed, performance improved, handling of S3 objects and methods across the package was harmonized, and a set of argument checks has been added. Starting with REndo 2.0, all functions support the use of transformations such as I(x^2) or log(x) in the formulas. Moreover, the call of most of the functions (except latentIV() and multilevelIV()) changed from the previous versions, making use of the Formula package.

Check the NEWS file or our github page for the latest updates and for reporting issues.

See our publication in the Journal of Statistical Software for more details: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v107.i03")}.

References

Gui R, Meierer M, Schilter P, Algesheimer R (2023). “REndo: Internal Instrumental Variables to Address Endogeneity.” Journal of Statistical Software, 107 (3), 1-43. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v107.i03")}

REndo documentation built on Sept. 8, 2023, 5:53 p.m.