Three-part right-hand side
formulas are supported now to facilitate specification of models with many exogenous regressors. For example, if there is one exogenous regressor
ex and one endogenous regressor
en with instrument
in, a formula with three parts on the right-hand side can now also be used:
y ~ ex | en | in. This is equivalent to specifying:
y ~ en + ex | in + ex.
Robust-regression estimators are provided as an alternative to ordinary least squares (OLS) both in stage 1 and 2 by means of
rlm() from package MASS. Specifically, in addition to 2-stage least squares (2SLS,
method = "OLS", default)
ivreg() now supports 2-stage M-estimation (2SM,
method = "M") and 2-stage MM-estimation (2SMM,
method = "MM").
Include information about which
"regressors" are endogenous variables and which
"instruments" are instruments for the endogenous variables in the fitted model objects from
ivreg.fit(). Both provide elements
$instruments which are named integer vectors provided that endogenous/instrument variables exist, and integers of length zero if not.
df.residual1 element in
ivreg objects with the residual degrees of freedom from the stage-1 regression.
coef(..., component = "stage1"),
vcov(..., component = "stage1"), and
confint(..., component = "stage1") for the estimated coefficients and corresponding variance-covariance matrix and confidence intervals from the stage-1 regression (only for the endogenous regressors). (Prompted by a request from Grant McDermott.)
residuals(..., type = "stage1") with the residuals from the stage-1 regression (only for the endogenous regressors).
confint() methods gained a
complete = TRUE argument assuring that the elements pertaining to aliased coefficients are included. By setting
complete = FALSE these elements are dropped.
Small edits to the Diagnostics vignette.
Initial version of the
ivreg package: An implementation of instrumental variables regression using two-stage least-squares (2SLS) estimation, based on the
ivreg() function previously in the AER package. In addition to standard regression functionality (parameter estimation, inference, predictions, etc.) the package provides various regression diagnostics, including hat values, deletion diagnostics such as studentized residuals and Cook’s distances; graphical diagnostics such as component-plus-residual plots and added-variable plots; and effect plots with partial residuals.
An overview of the package, documentation, examples, and vignettes are provided at https://john-d-fox.github.io/ivreg/.