### R FAQ How can I perform seemingly unrelated regression in R?

A single model may contain a number of linear equations. In such a model, it is often unrealistic to expect that the equation errors would be uncorrelated. A set of equations that has contemporaneous cross-equation error correlation (i.e. the error terms in the regression equations are corrlated) is called a seemingly unrelated regression (SUR) system. At first look, the equations seem unrelated, but the equations are related through the correlation in the errors. The systemfit R package allows a user to specify multiple equations and fit them in an SUR. After doing so, one can perform tests on coefficients across the equations.

We will illustrate SUR using the hsb2 dataset, predicting read and math with the overlapping sets of coefficients and then comparing some coefficients across the two equations. We will first define our model equations as R formulas.

library(foreign)
library(systemfit)

r1 <- read~female + as.numeric(ses) + socst
r2 <- math~female + as.numeric(ses) + science


Once the equations have been defined, they can be passed in a list to the systemfit command. A summary of the systemfit first shows a summary of the system (where N = 400), then the separate equations, and then how the residuals of the two equations are related. These are followed by the OLS fits of the separate equations.

fitsur <- systemfit(list(readreg = r1, mathreg = r2), data=hsb2)
summary(fitsur)

systemfit results
method: OLS

N  DF     SSR detRCov   OLS-R2 McElroy-R2
system 400 392 22835.2 3227.86 0.405103   0.342707

N  DF     SSR     MSE   RMSE       R2   Adj R2
readreg 200 196 12550.9 64.0351 8.0022 0.400037 0.390854
mathreg 200 196 10284.4 52.4712 7.2437 0.411171 0.402158

The covariance matrix of the residuals
mathreg 11.4952 52.4712

The correlations of the residuals
mathreg 0.198310 1.000000

OLS estimates for 'readreg' (equation 1)
Model Formula: read ~ female + as.numeric(ses) + socst

Estimate Std. Error  t value   Pr(>|t|)
(Intercept)     20.6824980  2.9789550  6.94287 5.5018e-11 ***
femalefemale    -1.5111280  1.1510793 -1.31279    0.19079
as.numeric(ses)  1.2183658  0.8399004  1.45061    0.14849
socst            0.5699327  0.0562967 10.12373 < 2.22e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8.002195 on 196 degrees of freedom
Number of observations: 200 Degrees of Freedom: 196
SSR: 12550.883066 MSE: 64.035118 Root MSE: 8.002195
Multiple R-Squared: 0.400037 Adjusted R-Squared: 0.390854

OLS estimates for 'mathreg' (equation 2)
Model Formula: math ~ female + as.numeric(ses) + science

Estimate Std. Error  t value   Pr(>|t|)
(Intercept)     19.305181   2.998047  6.43925 9.0557e-10 ***
femalefemale     1.160903   1.041641  1.11449   0.266432
as.numeric(ses)  1.399639   0.742390  1.88531   0.060867 .
science          0.575330   0.054328 10.58993 < 2.22e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.243704 on 196 degrees of freedom
Number of observations: 200 Degrees of Freedom: 196
SSR: 10284.364144 MSE: 52.471246 Root MSE: 7.243704
Multiple R-Squared: 0.411171 Adjusted R-Squared: 0.402158


We may be interested in comparing the effect of female on read, controlling for ses and socst, to the effect of female on math, controlling for ses and science. For this, we will use the linear.hypothesis command from the car package. To do this, we create a "restriction" on the model system. We will force the coefficient of female to be the same in both equations and then compare such a system fit to the one seen when the coefficients are not equal.

library(car)
linear.hypothesis(fitsur, restriction, test = "Chisq")

Linear hypothesis test (Chi^2 statistic of a Wald test)

Hypothesis:

Model 1: restricted model
Model 2: fitsur

Res.Df Df  Chisq Pr(>Chisq)
1    393
2    392  1 2.9626    0.08521 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

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