Inference Methods for Partially Linear Estimators.

Inference methods for partially linear estimators. Simple wrapper for sandwich::vcovHC() and sandwich::vcovCL(). Default standard errors are heteroskedasiticty-robust. If the ddml estimator was computed using a cluster_variable, the standard errors are also cluster-robust by default.

Usage

# S3 method for class 'ddml_fpliv'
summary(object, ...)

# S3 method for class 'ddml_pliv'
summary(object, ...)

# S3 method for class 'ddml_plm'
summary(object, ...)

Arguments

object: An object of class ddml_plm, ddml_pliv, or ddml_fpliv as fitted by ddml_plm(), ddml_pliv(), and ddml_fpliv(), respectively.
...: Additional arguments passed to vcovHC and vcovCL. See sandwich::vcovHC() and sandwich::vcovCL() for a complete list of arguments.

Value

An array with inference results for each ensemble_type.

References

Zeileis A (2004). "Econometric Computing with HC and HAC Covariance Matrix Estimators.” Journal of Statistical Software, 11(10), 1-17.

Zeileis A (2006). “Object-Oriented Computation of Sandwich Estimators.” Journal of Statistical Software, 16(9), 1-16.

Zeileis A, Köll S, Graham N (2020). “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.” Journal of Statistical Software, 95(1), 1-36.

Examples

# Construct variables from the included Angrist & Evans (1998) data
y = AE98[, "worked"]
D = AE98[, "morekids"]
X = AE98[, c("age","agefst","black","hisp","othrace","educ")]

# Estimate the partially linear model using a single base learner, ridge.
plm_fit <- ddml_plm(y, D, X,
                    learners = list(what = mdl_glmnet,
                                    args = list(alpha = 0)),
                    sample_folds = 2,
                    silent = TRUE)
summary(plm_fit)
#> PLM estimation results: 
#>  
#> , , single base learner
#> 
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  0.00064    0.00688   0.093 9.26e-01
#> D_r         -0.14675    0.01473  -9.962 2.25e-23
#>