library( systemfit )
     data( kmenta )
#     q food consumption per capita.
#    p ratio of food prices to general consumer prices.
# d disposable income in constant dollars.
#     f ratio of preceding year's prices received by farmers to general
#          consumer prices.
#     a time in years.

     demand <- q ~ p + d
     supply <- q ~ p + f + a
     labels <- list( "demand", "supply" )
     system <- list( demand, supply )

     ## OLS estimation
     fitols <- systemfit("OLS", system, labels, data=kmenta )
     print( fitols )

     ## OLS estimation with 2 restrictions
     Rrestr <- matrix(0,2,7)
     qrestr <- matrix(0,2,1)
     Rrestr[1,3] <-  1
     Rrestr[1,7] <- -1
     Rrestr[2,2] <- -1
     Rrestr[2,5] <-  1
     qrestr[2,1] <-  0.5
Rrestr
#    0    0    1    0    0    0   -1
#    0   -1    0    0    1    0    0
qrestr
# 0.0
# 0.5
#Rb=q  ,means b3-b7=0 and  b5-b2=0.5 are two restrictions
     fitols2 <- systemfit("OLS", system, labels, data=kmenta,
                           R.restr=Rrestr, q.restr=qrestr )
     print( fitols2 )





     ## iterated SUR estimation
     fitsur <- systemfit("SUR", system, labels, data=kmenta, maxit=100 )
     print( fitsur )

     ## 2SLS estimation
     inst <- ~ d + f + a
     fit2sls <- systemfit( "2SLS", system, labels, inst, kmenta )
     print( fit2sls )

     ## 2SLS estimation with different instruments in each equation
     inst1 <- ~ d + f
     inst2 <- ~ d + f + a
     instlist <- list( inst1, inst2 )
     fit2sls2 <- systemfit( "2SLS", system, labels, instlist, kmenta )
     print( fit2sls2 )

     ## 3SLS estimation with GMM-3SLS formula
     inst <- ~ d + f + a
     fit3sls <- systemfit( "3SLS", system, labels, inst, kmenta, formula3sls="GMM" )
     print( fit3sls )



How to do the Klein I model with systemfit software?
Unsolved problem:

Some Hints:
library( systemfit )
     data( klein )
p.lag <- c(NA, p[-length(p)])
x.lag <- c(NA, x[-length(x)])
a <- year - 1931
cbind(year, a, p, p.lag, x, x.lag)
#NEED to write system of equations.  Should it be 3 or include definitional
#identities?
eqn.1 <- tsls(c ~ p + p.lag + I(wp + wg), 
    instruments= ~ g + t + wg + a + p.lag + k.lag + x.lag)
summary(eqn.1)

eqn.2 <- tsls(i ~ p + p.lag + k.lag,
    instruments= ~ g + t + wg + a + p.lag + k.lag + x.lag)
summary(eqn.2)