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Last data update: 2014.03.03

R: Methods for Instrumental-Variable Regression

summary.ivreg

R Documentation

Methods for Instrumental-Variable Regression

Description

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

Usage

## S3 method for class 'ivreg'
summary(object, vcov. = NULL, df = NULL, diagnostics = FALSE, ...)
## S3 method for class 'ivreg'
anova(object, object2, test = "F", vcov = NULL, ...)

## S3 method for class 'ivreg'
terms(x, component = c("regressors", "instruments"), ...)
## S3 method for class '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, bread, estfun) is available.

Examples

## 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")

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)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> 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/summary.ivreg.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summary.ivreg
> ### Title: Methods for Instrumental-Variable Regression
> ### Aliases: summary.ivreg print.summary.ivreg vcov.ivreg bread.ivreg
> ###   estfun.ivreg anova.ivreg hatvalues.ivreg predict.ivreg terms.ivreg
> ###   model.matrix.ivreg
> ### 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)
> 
> ## model 
> fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi),
+   data = CigarettesSW, subset = year == "1995")
> summary(fm)

Call:
ivreg(formula = log(packs) ~ log(rprice) + log(rincome) | log(rincome) + 
    tdiff + I(tax/cpi), data = CigarettesSW, subset = year == 
    "1995")

Residuals:
       Min         1Q     Median         3Q        Max 
-0.6006931 -0.0862222 -0.0009999  0.1164699  0.3734227 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    9.8950     1.0586   9.348 4.12e-12 ***
log(rprice)   -1.2774     0.2632  -4.853 1.50e-05 ***
log(rincome)   0.2804     0.2386   1.175    0.246    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1879 on 45 degrees of freedom
Multiple R-Squared: 0.4294,	Adjusted R-squared: 0.4041 
Wald test: 13.28 on 2 and 45 DF,  p-value: 2.931e-05 

> summary(fm, vcov = sandwich, df = Inf, diagnostics = TRUE)

Call:
ivreg(formula = log(packs) ~ log(rprice) + log(rincome) | log(rincome) + 
    tdiff + I(tax/cpi), data = CigarettesSW, subset = year == 
    "1995")

Residuals:
       Min         1Q     Median         3Q        Max 
-0.6006931 -0.0862222 -0.0009999  0.1164699  0.3734227 

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)    9.8950     0.9288  10.654  < 2e-16 ***
log(rprice)   -1.2774     0.2417  -5.286 1.25e-07 ***
log(rincome)   0.2804     0.2458   1.141    0.254    

Diagnostic tests:
                 df1 df2 statistic p-value    
Weak instruments   2  44   228.738  <2e-16 ***
Wu-Hausman         1  44     3.823  0.0569 .  
Sargan             1  NA     0.333  0.5641    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1879 on Inf degrees of freedom
Multiple R-Squared: 0.4294,	Adjusted R-squared: 0.4041 
Wald test: 34.51 on 2 DF,  p-value: 3.214e-08 

> 
> ## ANOVA
> fm2 <- ivreg(log(packs) ~ log(rprice) | tdiff, data = CigarettesSW, subset = year == "1995")
> anova(fm, fm2, vcov = sandwich, test = "Chisq")
Wald test

Model 1: log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + 
    I(tax/cpi)
Model 2: log(packs) ~ log(rprice) | tdiff
  Res.Df Df  Chisq Pr(>Chisq)
1     45                     
2     46 -1 1.3011      0.254
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>