Package: REndo 2.4.10

Raluca Gui

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

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> heteroscedasticity 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. See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>. Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.

Authors:Raluca Gui [cre, aut], Markus Meierer [aut], Rene Algesheimer [aut], Patrik Schilter [aut]

REndo_2.4.10.tar.gz
REndo_2.4.10.tar.gz(r-4.5-noble)REndo_2.4.10.tar.gz(r-4.4-noble)
REndo_2.4.10.tgz(r-4.4-emscripten)REndo_2.4.10.tgz(r-4.3-emscripten)
REndo.pdf |REndo.html✨
REndo/json (API)
NEWS

# InstallREndo in R:
install.packages('REndo',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mmeierer/rendo/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • dataCopCont - Simulated Dataset with One Endogenous Continuous Regressor
  • dataCopCont2 - Simulated Dataset with Two Endogenous Continuous Regressor
  • dataCopDis - Simulated Dataset with One Endogenous Discrete Regressor
  • dataCopDis2 - Simulated Dataset with Two Endogenous Discrete Regressors
  • dataCopDisCont - Simulated Dataset with Two Endogenous Regressors
  • dataHetIV - Simulated Dataset with One Endogenous Continuous Regressor
  • dataHigherMoments - Simulated Dataset with One Endogenous Regressor
  • dataLatentIV - Simulated Dataset with One Endogenous Continuous Regressor
  • dataMultilevelIV - Multilevel Simulated Dataset - Three Levels

5 exports 2 stars 0.49 score 69 dependencies 1.3k downloads

Last updated 3 days agofrom:390f40a9bd

Exports:copulaCorrectionhetErrorsIVhigherMomentsIVlatentIVmultilevelIV

Dependencies:abindAERbackportsbootbroomcarcarDataclicolorspacecorpcorcowplotcpp11data.tableDerivdoBydplyrfansifarverFormulagenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellmvtnormnlmenloptrnnetnumDerivoptimxpbkrtestpillarpkgconfigpracmapurrrquantregR6RColorBrewerRcppRcppEigenrlangsandwichscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

REndo-introduction

Rendered fromREndo-introduction.pdf.asisusingR.rsp::asison Jul 03 2024.

Last update: 2019-01-16
Started: 2019-01-16

Readme and manuals

Help Manual

Help pageTopics
Confidence Intervals for Bootstrapped Model Parametersconfint.rendo.boots
Fitting Linear Models Endogenous Regressors using Gaussian CopulacopulaCorrection
Simulated Dataset with One Endogenous Continuous RegressordataCopCont
Simulated Dataset with Two Endogenous Continuous RegressordataCopCont2
Simulated Dataset with One Endogenous Discrete RegressordataCopDis
Simulated Dataset with Two Endogenous Discrete RegressorsdataCopDis2
Simulated Dataset with Two Endogenous RegressorsdataCopDisCont
Simulated Dataset with One Endogenous Continuous RegressordataHetIV
Simulated Dataset with One Endogenous RegressordataHigherMoments
Simulated Dataset with One Endogenous Continuous RegressordataLatentIV
Multilevel Simulated Dataset - Three LevelsdataMultilevelIV
Fitting Linear Models with Endogenous Regressors using Heteroskedastic Covariance RestrictionshetErrorsIV
Fitting Linear Models with Endogenous Regressors using Lewbel's Higher Moments ApproachhigherMomentsIV
Fitting Linear Models with one Endogenous Regressor using Latent Instrumental VariableslatentIV
Fitting Multilevel GMM Estimation with Endogenous RegressorsmultilevelIV
Predict method for Models using the Gaussian Copula Approachpredict.rendo.copula.correction
Predict method for fitted Regression Models with Internal Instrumental Variablespredict.rendo.ivreg
Predict method for Models using the Latent Instrumental Variables approachpredict.rendo.latent.IV
Predict method for Multilevel GMM Estimationspredict.rendo.multilevel
Fitting Linear Models with Endogenous Regressors using Latent Instrumental VariablesREndo-package REndo
Summarizing Bootstrapped copulaCorrection Model Fitssummary.rendo.copula.correction
Summarizing latentIV Model Fitssummary.rendo.latent.IV
Summarizing Multilevel GMM Estimation with Endogenous Regressors Model Fitssummary.rendo.multilevel
Calculate Variance-Covariance Matrix for Models Fitted with Bootstrapped Parametersvcov.rendo.boots