NEWS.md
Enhanced predict.ivreg() method, which optionally provides standard errors, confidence intervals, and prediction intervals for predicted values.
The tinytable rather than the kableExtra package (recently not actively maintained) is used now for the modelsummary table shown in the package vignette (contributed by Vincent Arel-Bundock).
Further small improvements in the package vignettes.
Improve non-anchored links in manual pages (prompted by CRAN).
Achim Zeileis took over maintenance, both on CRAN and on GitHub. The GitHub source repository is now at https://github.com/zeileis/ivreg/ with the web page at https://zeileis.github.io/ivreg/.
Avoid partial argument matches by calling model.matrix(..., contrasts.arg = ...) rather than just contrasts (reported by Kevin Tappe).
Make names of arguments of influencePlot.ivreg() and outlierTest.ivreg() consistent with the corresponding generic functions from the car package.
method is now an explicit argument to ivreg() and not just passed through ... to ivreg.fit().
More efficient computation of regression diagnostics (thanks to improvements implemented by Nikolas Kuschnig).
In models without any exogenous variables (i.e., not even an exogenous (Intercept)) the $instruments element in the fitted model object was erroneously empty, leading to some incorrect subsequent computations. Also the $endogenous element was an unnamed (rather than named) vector. Both problems have been fixed now. (Reported by Luke Sonnet.)
In the summary() method the default is now diagnostics = NULL (rather than always TRUE). It is now only set to TRUE if there are both endogenous and instrument variables, and FALSE otherwise. (Reported by Brantly Callaway.)
Small fixes.
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").
Dedicated confint() method allowing specification of the variance-covariance matrix vcov. and degrees of freedom df to be used (as in the summary() method).
Include information about which "regressors" are endogenous variables and which "instruments" are instruments for the endogenous variables in the fitted model objects from ivreg() and ivreg.fit(). Both provide elements $endogenous and $instruments which are named integer vectors provided that endogenous/instrument variables exist, and integers of length zero if not.
Include df.residual1 element in ivreg objects with the residual degrees of freedom from the stage-1 regression.
Add 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.)
Add residuals(..., type = "stage1") with the residuals from the stage-1 regression (only for the endogenous regressors).
The coef(), vcov(), and 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.
Include demonstration how to use ivreg() results in model summary tables and plots using the modelsummary package.
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/.