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Last data update: 2014.03.03

R: Fitting Instrumental-Variable Regressions

ivreg.fit

R Documentation

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 `link[stats]{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$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$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$y
x <- model.matrix(fm, component = "regressors")
z <- model.matrix(fm, component = "instruments")
ivreg.fit(x, y, z)$coefficients

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AER)
Loading required package: car
Loading required package: lmtest
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/ivreg.fit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ivreg.fit
> ### Title: Fitting Instrumental-Variable Regressions
> ### Aliases: ivreg.fit
> ### Keywords: regression
> 
> ### ** 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, component = "regressors")
> z <- model.matrix(fm, component = "instruments")
> ivreg.fit(x, y, z)$coefficients
 (Intercept)  log(rprice) log(rincome) 
   9.8949555   -1.2774241    0.2804048 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>