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AER

summary.ivreg

Methods for Instrumental-Variable Regression

Description

Methods to standard generics for instrumental-variable regressions fitted by ivreg.

Usage

## S3 method for class 'ivreg'
summary(object, vcov. = NULL, df = NULL, diagnostics = FALSE, ...)
## S3 method for class 'ivreg'
anova(object, object2, test = "F", vcov = NULL, ...)

## S3 method for class 'ivreg'
terms(x, component = c("regressors", "instruments"), ...)
## S3 method for class 'ivreg'
model.matrix(object, component = c("projected", "regressors", "instruments"), ...)

Arguments

`object, object2, x`	an object of class `"ivreg"` as fitted by `ivreg`.
`vcov., vcov`	a specification of the covariance matrix of the estimated coefficients. This can be specified as a matrix or as a function yielding a matrix when applied to the fitted model. If it is a function it is also employed in the two diagnostic F tests (if `diagnostics = TRUE` in the `summary()` method).
`df`	the degrees of freedom to be used. By default this is set to residual degrees of freedom for which a t or F test is computed. Alternatively, it can be set to `Inf` (or equivalently `0`) for which a z or Chi-squared test is computed.
`diagnostics`	logical. Should diagnostic tests for the instrumental-variable regression be carried out? These encompass an F test of the first stage regression for weak instruments, a Wu-Hausman test for endogeneity, and a Sargan test of overidentifying restrictions (only if there are more instruments than regressors).
`test`	character specifying whether to compute the large sample Chi-squared statistic (with asymptotic Chi-squared distribution) or the finite sample F statistic (with approximate F distribution).
`component`	character specifying for which component of the terms or model matrix should be extracted. `"projected"` gives the matrix of regressors projected on the image of the instruments.
`...`	currently not used.

Details

ivreg is the high-level interface to the work-horse function ivreg.fit, a set of standard methods (including summary, vcov, anova, hatvalues, predict, terms, model.matrix, update, bread, estfun) is available.

Examples

## data
data("CigarettesSW")
CigarettesSW <- transform(CigarettesSW,
  rprice  = price/cpi,
  rincome = income/population/cpi,
  tdiff   = (taxs - tax)/cpi
)

## model 
fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi),
  data = CigarettesSW, subset = year == "1995")
summary(fm)
summary(fm, vcov = sandwich, df = Inf, diagnostics = TRUE)

## ANOVA
fm2 <- ivreg(log(packs) ~ log(rprice) | tdiff, data = CigarettesSW, subset = year == "1995")
anova(fm, fm2, vcov = sandwich, test = "Chisq")

AER

Applied Econometrics with R

v1.2-10

GPL-2 | GPL-3

Authors

Christian Kleiber [aut] (<https://orcid.org/0000-0002-6781-4733>), Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>)

Initial release

2022-06-13