High-Dimensional Metrics
Coefficients from S3 objects rlassoEffects
Coefficients from S3 objects rlassoIV
Coefficients from S3 objects rlassoIVselectX
Coefficients from S3 objects rlassoIVselectZ
Growth data set
hdm: High-Dimensional Metrics
Function for Calculation of the penalty parameter
Shooting Lasso
Methods for S3 object rlasso
Methods for S3 object rlassoEffects
Methods for S3 object rlassoIV
Methods for S3 object rlassoIVselectX
Methods for S3 object rlassoIVselectZ
Methods for S3 object rlassologit
Methods for S3 object rlassologitEffects
Methods for S3 object rlassoTE
Methods for S3 object tsls
Multiple Testing Adjustment of p-values for S3 objects rlassoEffects...
Printing coefficients from S3 objects rlassoEffects
rlasso: Function for Lasso estimation under homoscedastic and heterosc...
rigorous Lasso for Linear Models: Inference
Post-Selection and Post-Regularization Inference in Linear Models with...
Instrumental Variable Estimation with Selection on the exogenous Varia...
Instrumental Variable Estimation with Lasso
rlassologit: Function for logistic Lasso estimation
rigorous Lasso for Logistic Models: Inference
Summarizing rlassoEffects fits
Functions for estimation of treatment effects
Two-Stage Least Squares Estimation (TSLS)
Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.