The ivreg function for instrumental variables regression had first been introduced in the AER package but is now developed and extended in its own package of the same name. This post provides a short overview and illustration.

Package overview

The ivreg package (by John Fox, Christian Kleiber, and Achim Zeileis) provides a comprehensive implementation of instrumental variables regression using two-stage least-squares (2SLS) estimation. The standard regression functionality (parameter estimation, inference, robust covariances, predictions, etc.) is derived from and supersedes the ivreg() function in the AER package. Additionally, various regression diagnostics are supported, including hat values, deletion diagnostics such as studentized residuals and Cook’s distances; graphical diagnostics such as component-plus-residual plots and added-variable plots; and effect plots with partial residuals.

An overview of the package along with vignettes and detailed documentation etc. is available on its web site at https://john-d-fox.github.io/ivreg/. This post is an abbreviated version of the “Getting started” vignette.

The ivreg package integrates seamlessly with other packages by providing suitable S3 methods, specifically for generic functions in the base-R stats package, and in the car, effects, lmtest, and sandwich packages, among others. Moreover, it cooperates well with other object-oriented packages for regression modeling such as broom and modelsummary.

Illustration: Returns to schooling

For demonstrating the ivreg package in practice, we investigate the effect of schooling on earnings in a classical model for wage determination. The data are from the United States, and are provided in the package as SchoolingReturns. This data set was originally studied by David Card, and was subsequently employed, as here, to illustrate 2SLS estimation in introductory econometrics textbooks. The relevant variables for this illustration are:

data("SchoolingReturns", package = "ivreg")
summary(SchoolingReturns[, 1:8])
##       wage          education       experience     ethnicity     smsa     
##  Min.   : 100.0   Min.   : 1.00   Min.   : 0.000   other:2307   no : 864  
##  1st Qu.: 394.2   1st Qu.:12.00   1st Qu.: 6.000   afam : 703   yes:2146  
##  Median : 537.5   Median :13.00   Median : 8.000                          
##  Mean   : 577.3   Mean   :13.26   Mean   : 8.856                          
##  3rd Qu.: 708.8   3rd Qu.:16.00   3rd Qu.:11.000                          
##  Max.   :2404.0   Max.   :18.00   Max.   :23.000                          
##  south           age        nearcollege
##  no :1795   Min.   :24.00   no : 957   
##  yes:1215   1st Qu.:25.00   yes:2053   
##             Median :28.00              
##             Mean   :28.12              
##             3rd Qu.:31.00              
##             Max.   :34.00

A standard wage equation uses a semi-logarithmic linear regression for wage, estimated by ordinary least squares (OLS), with years of education as the primary explanatory variable, adjusting for a quadratic term in labor-market experience, as well as for factors coding ethnicity, residence in a city (smsa), and residence in the U.S. south:

m_ols <- lm(log(wage) ~ education + poly(experience, 2) + ethnicity + smsa + south,
  data = SchoolingReturns)
summary(m_ols)
## Call:
## lm(formula = log(wage) ~ education + poly(experience, 2) + ethnicity + 
##     smsa + south, data = SchoolingReturns)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.59297 -0.22315  0.01893  0.24223  1.33190 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           5.259820   0.048871 107.626  < 2e-16 ***
## education             0.074009   0.003505  21.113  < 2e-16 ***
## poly(experience, 2)1  8.931699   0.494804  18.051  < 2e-16 ***
## poly(experience, 2)2 -2.642043   0.374739  -7.050 2.21e-12 ***
## ethnicityafam        -0.189632   0.017627 -10.758  < 2e-16 ***
## smsayes               0.161423   0.015573  10.365  < 2e-16 ***
## southyes             -0.124862   0.015118  -8.259  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3742 on 3003 degrees of freedom
## Multiple R-squared:  0.2905, Adjusted R-squared:  0.2891 
## F-statistic: 204.9 on 6 and 3003 DF,  p-value: < 2.2e-16

Thus, OLS estimation yields an estimate of 7.4% per year for returns to schooling. This estimate is problematic, however, because it can be argued that education is endogenous (and hence also experience, which is taken to be age minus education minus 6). We therefore use geographical proximity to a college when growing up as an exogenous instrument for education. Additionally, age is the natural exogenous instrument for experience, while the remaining explanatory variables can be considered exogenous and are thus used as instruments for themselves. Although it’s a useful strategy to select an effective instrument or instruments for each endogenous explanatory variable, in 2SLS regression all of the instrumental variables are used to estimate all of the regression coefficients in the model.

To fit this model with ivreg() we can simply extend the formula from lm() above, adding a second part after the | separator to specify the instrumental variables:

library("ivreg")
m_iv <- ivreg(log(wage) ~ education + poly(experience, 2) + ethnicity + smsa + south |
  nearcollege + poly(age, 2) + ethnicity + smsa + south,
  data = SchoolingReturns)

Equivalently, the same model can also be specified slightly more concisely using three parts on the right-hand side indicating the exogenous variables, the endogenous variables, and the additional instrumental variables only (in addition to the exogenous variables).

m_iv <- ivreg(log(wage) ~ ethnicity + smsa + south | education + poly(experience, 2) |
  nearcollege + poly(age, 2), data = SchoolingReturns)

Both models yield the following results:

summary(m_iv)
## Call:
## ivreg(formula = log(wage) ~ education + poly(experience, 2) + 
##     ethnicity + smsa + south | nearcollege + poly(age, 2) + ethnicity + 
##     smsa + south, data = SchoolingReturns)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.82400 -0.25248  0.02286  0.26349  1.31561 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           4.48522    0.67538   6.641 3.68e-11 ***
## education             0.13295    0.05138   2.588 0.009712 ** 
## poly(experience, 2)1  9.14172    0.56350  16.223  < 2e-16 ***
## poly(experience, 2)2 -0.93810    1.58024  -0.594 0.552797    
## ethnicityafam        -0.10314    0.07737  -1.333 0.182624    
## smsayes               0.10798    0.04974   2.171 0.030010 *  
## southyes             -0.09818    0.02876  -3.413 0.000651 ***
## 
## Diagnostic tests:
##                                          df1  df2 statistic  p-value    
## Weak instruments (education)               3 3003     8.008 2.58e-05 ***
## Weak instruments (poly(experience, 2)1)    3 3003  1612.707  < 2e-16 ***
## Weak instruments (poly(experience, 2)2)    3 3003   174.166  < 2e-16 ***
## Wu-Hausman                                 2 3001     0.841    0.432    
## Sargan                                     0   NA        NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4032 on 3003 degrees of freedom
## Multiple R-Squared: 0.1764,  Adjusted R-squared: 0.1747 
## Wald test: 148.1 on 6 and 3003 DF,  p-value: < 2.2e-16

Thus, using two-stage least squares to estimate the regression yields a much larger coefficient for the returns to schooling, namely 13.3% per year. Notice as well that the standard errors of the coefficients are larger for 2SLS estimation than for OLS, and that, partly as a consequence, evidence for the effects of ethnicity and the quadratic component of experience is now weak. These differences are brought out more clearly when showing coefficients and standard errors side by side, e.g., using the compareCoefs() function from the car package or the msummary() function from the modelsummary package:

library("modelsummary")
m_list <- list(OLS = m_ols, IV = m_iv)
msummary(m_list)

The change in coefficients and associated standard errors can also be brought out graphically using the modelplot() function from modelsummary which shows the coefficient estimates along with their 95% confidence intervals. Below we omit the intercept and experience terms as these are on a different scale than the other coefficients.

modelplot(m_list, coef_omit = "Intercept|experience")