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Inference Methods for Partially Linear Estimators.

Source: R/ddml_fpliv.R, R/ddml_pliv.R, R/ddml_plm.R
summary.ddml_plm.Rd

Inference methods for partially linear estimators. Simple wrapper for sandwich::vcovHC().

Usage

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

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

# S3 method for 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. See sandwich::vcovHC() 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.

See also

sandwich::vcovHC()

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) -0.001067536 0.006894873 -0.1548304 8.769550e-01
#> D_r         -0.146714549 0.014758777 -9.9408337 2.765033e-23
#>