AER (version 0.2-2)

ivreg.fit: Fitting Intrumental-Variable Regressions

Description

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.

Usage

ivreg.fit(x, y, z, weights, offset, ...)

Arguments

x

regressor matrix.

y

vector with dependet variable.

z

instruments matrix.

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.

...

further arguments passed to lm.fit or link[stats]{lm.wfit}, respectively.

Value

• ivreg.fit returns an unclassed list 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).
• estfun
  a matrix containing the empirical estimating functions.
• 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.

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, bread, estfun) is available and described on summary.ivreg. ivreg.fit is a convenience interface to lm.fit (or lm.wfit) for first projecting x onto the image of z and the running a regression of y onto the projected x.

See Also

ivreg, lm.fit

Examples

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

## high-level interface
fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi),
  data = CigarettesSW, subset = year == "1995")

## low-level interface
y <- fm$y
x <- model.matrix(fm)
z <- model.matrix(fm, component = "instruments")
ivreg.fit(x, y, z)$coefficients