AER (version 1.2-9)

summary.ivreg: Methods for Instrumental-Variable Regression

Description

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

Usage

# S3 method for ivreg
summary(object, vcov. = NULL, df = NULL, diagnostics = FALSE, …)
# S3 method for ivreg
anova(object, object2, test = "F", vcov = NULL, …)
# S3 method for ivreg
terms(x, component = c("regressors", "instruments"), …)
# S3 method for 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.

See Also

ivreg, lm.fit

Examples

# NOT RUN {
## data
data("CigarettesSW")
CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (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")
# }