AER: ivreg.fit – R documentation

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ivreg.fit

Fitting Instrumental-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 dependent 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 `lm.wfit`, respectively.

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.

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.
`nobs`	number of observations with non-zero weights.
`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.

Examples

## data
data("CigarettesSW")
CigarettesSW <- transform(CigarettesSW,
  rprice  = price/cpi,
  rincome = income/population/cpi,
  tdiff   = (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$y
x <- model.matrix(fm, component = "regressors")
z <- model.matrix(fm, component = "instruments")
ivreg.fit(x, y, z)$coefficients

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