| ivreg {AER} | R Documentation |
Fit instrumental-variable regression by two-stage least squares. This is equivalent to direct instrumental-variables estimation when the number of instruments is equal to the number of predictors.
ivreg(formula, instruments, data, subset, na.action, weights, offset, contrasts = NULL, model = TRUE, y = TRUE, x = FALSE, ...)
formula, instruments |
formula specification(s) of the regression
relationship and the instruments. Either instruments is missing and
formula has three parts as in y ~ x1 + x2 | z1 + z2 + z3
(recommended) or formula is y ~ x1 + x2 and instruments
is a one-sided formula ~ z1 + z2 + z3 (only for backward compatibility). |
data |
an optional data frame containing the variables in the model.
By default the variables are taken from the environment from which ivreg is
called. |
subset |
an optional vector specifying a subset of observations to be used in fitting the model. |
na.action |
a function that indicates what should happen when the
data contain NAs. The default is set by the na.action option. |
weights |
an optional vector of weights to be used in the fitting process. |
offset |
an optional offset that can be used to specify an a priori known component to be included during fitting. |
contrasts |
an optional list. See the contrasts.arg of
model.matrix.default. |
model, x, y |
logicals. If TRUE the corresponding components of
the fit (the model frame, the model matrices , the response) are returned. |
... |
further arguments passed to ivreg.fit. |
ivreg is the high-level interface to the work-horse function ivreg.fit,
a set of standard methods (including print, summary, vcov, anova,
hatvalues, predict, terms, model.matrix, bread,
estfun) is available and described on summary.ivreg.
ivreg returns an object of class "ivreg", with the following components:
coefficients |
parameter estimates. |
residuals |
a vector of residuals. |
fitted.values |
a vector of predicted means. |
weights |
either the vector of weights used (if any) or NULL (if none). |
offset |
either the offset used (if any) or NULL (if none). |
n |
number of observations. |
rank |
the numeric rank of the fitted linear model. |
df.residual |
residual degrees of freedom for fitted model. |
cov.unscaled |
unscaled covariance matrix for the coefficients. |
sigma |
residual standard error. |
hatvalues |
regression hat values. |
call |
the original function call. |
formula |
the model formula. |
terms |
a list with elements "regressors" and "instruments"
containing the terms objects for the respective components. |
levels |
levels of the categorical regressors. |
contrasts |
the contrasts used for categorical regressors. |
model |
the full model frame (if model = TRUE). |
y |
the response vector (if y = TRUE). |
x |
a list with elements "regressors", "instruments", "projected",
containing the model matrices from the respective components
(if x = TRUE). "projected" is the matrix of regressors projected
on the image of the instruments. |
Greene, W. H. (1993) Econometric Analysis, 2nd ed., Macmillan.
## 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)
## ANOVA
fm2 <- ivreg(log(packs) ~ log(rprice) | tdiff, data = CigarettesSW, subset = year == "1995")
anova(fm, fm2)