SYSLIN Procedure

Example 35.1 Klein’s Model I Estimated with LIML and 3SLS

(View the complete code for this example.)

This example uses PROC SYSLIN to estimate the classic Klein Model I. For a discussion of this model, see Theil (1971). The following statements read the data:

*---------------------------Klein's Model I----------------------------*
| By L.R. Klein, Economic Fluctuations in the United States, 1921-1941 |
| (1950), NY: John Wiley.   A macro-economic model of the U.S. with    |
| three behavioral equations, and several identities. See Theil, p.456.|
*----------------------------------------------------------------------*;
data klein;
input year c p w i x wp g t k wsum;
   date=mdy(1,1,year);
   format date monyy.;
   y   =c+i+g-t;
   yr  =year-1931;
   klag=lag(k);
   plag=lag(p);
   xlag=lag(x);
   label year='Year'
         date='Date'
         c   ='Consumption'
         p   ='Profits'
         w   ='Private Wage Bill'
         i   ='Investment'
         k   ='Capital Stock'
         y   ='National Income'
         x   ='Private Production'
         wsum='Total Wage Bill'
         wp  ='Govt Wage Bill'
         g   ='Govt Demand'
         i   ='Taxes'
         klag='Capital Stock Lagged'
         plag='Profits Lagged'
         xlag='Private Product Lagged'
         yr  ='YEAR-1931';
datalines;
1920     .  12.7     .    .  44.9    .     .     .  182.8     .
1921  41.9  12.4  25.5 -0.2  45.6  2.7   3.9   7.7  182.6  28.2
1922  45.0  16.9  29.3  1.9  50.1  2.9   3.2   3.9  184.5  32.2
1923  49.2  18.4  34.1  5.2  57.2  2.9   2.8   4.7  189.7  37.0
1924  50.6  19.4  33.9  3.0  57.1  3.1   3.5   3.8  192.7  37.0
1925  52.6  20.1  35.4  5.1  61.0  3.2   3.3   5.5  197.8  38.6
1926  55.1  19.6  37.4  5.6  64.0  3.3   3.3   7.0  203.4  40.7
1927  56.2  19.8  37.9  4.2  64.4  3.6   4.0   6.7  207.6  41.5
1928  57.3  21.1  39.2  3.0  64.5  3.7   4.2   4.2  210.6  42.9
1929  57.8  21.7  41.3  5.1  67.0  4.0   4.1   4.0  215.7  45.3
1930  55.0  15.6  37.9  1.0  61.2  4.2   5.2   7.7  216.7  42.1

   ... more lines ...   

The following statements estimate the Klein model using the limited information maximum likelihood method. In addition, the parameter estimates are written to a SAS data set with the OUTEST= option.

proc syslin data=klein outest=b liml;
   endogenous c p w i x wsum k y;
   instruments klag plag xlag wp g t yr;
   consume: model c = p plag  wsum;
   invest:  model i = p plag  klag;
   labor:   model w = x xlag  yr;
run;
proc print data=b;
run;

The PROC SYSLIN estimates are shown in Output 35.1.1 through Output 35.1.3.

Output 35.1.1: LIML Estimates for Consumption

The SYSLIN Procedure
Limited-Information Maximum Likelihood Estimation

ModelCONSUME
Dependent Variablec
LabelConsumption

Analysis of Variance
SourceDFSum of SquaresMean SquareF ValuePr > F
Model3854.3541284.7847118.42<.0001
Error1740.884192.404952  
Corrected Total20941.4295   

Root MSE1.55079R-Square0.95433
Dependent Mean53.99524Adj R-Sq0.94627
Coeff Var2.87209  

Parameter Estimates
VariableDFParameter
Estimate
Standard
Error
t ValuePr > |t|Variable
Label
Intercept117.147652.0453748.38<.0001Intercept
p1-0.222510.224230-0.990.3349Profits
plag10.3960270.1929432.050.0558Profits Lagged
wsum10.8225590.06154913.36<.0001Total Wage Bill


Output 35.1.2: LIML Estimates for Investments

The SYSLIN Procedure
Limited-Information Maximum Likelihood Estimation

ModelINVEST
Dependent Variablei
LabelTaxes

Analysis of Variance
SourceDFSum of SquaresMean SquareF ValuePr > F
Model3210.379070.1263434.06<.0001
Error1734.996492.058617  
Corrected Total20252.3267   

Root MSE1.43479R-Square0.85738
Dependent Mean1.26667Adj R-Sq0.83221
Coeff Var113.27274  

Parameter Estimates
VariableDFParameter
Estimate
Standard
Error
t ValuePr > |t|Variable
Label
Intercept122.590839.4981462.380.0294Intercept
p10.0751850.2247120.330.7420Profits
plag10.6803860.2091453.250.0047Profits Lagged
klag1-0.168260.045345-3.710.0017Capital Stock Lagged


Output 35.1.3: LIML Estimates for Labor

The SYSLIN Procedure
Limited-Information Maximum Likelihood Estimation

ModelLABOR
Dependent Variablew
LabelPrivate Wage Bill

Analysis of Variance
SourceDFSum of SquaresMean SquareF ValuePr > F
Model3696.1485232.0495393.62<.0001
Error1710.021920.589525  
Corrected Total20794.9095   

Root MSE0.76781R-Square0.98581
Dependent Mean36.36190Adj R-Sq0.98330
Coeff Var2.11156  

Parameter Estimates
VariableDFParameter
Estimate
Standard
Error
t ValuePr > |t|Variable
Label
Intercept11.5261871.3208381.160.2639Intercept
x10.4339410.0755075.75<.0001Private Production
xlag10.1513210.0745272.030.0583Private Product Lagged
yr10.1315930.0359953.660.0020YEAR-1931


The OUTEST= data set is shown in part in Output 35.1.4. Note that the data set contains the parameter estimates and root mean squared errors, _SIGMA_, for the first-stage instrumental regressions as well as the parameter estimates and sigma for the LIML estimates for the three structural equations.

Output 35.1.4: The OUTEST= Data Set

Obs_TYPE__STATUS__MODEL__DEPVAR__SIGMA_Interceptklagplagxlagwpgtyrcpwixwsumky
1LIML0 ConvergedCONSUMEc1.5507917.1477.0.39603.....-1-0.22251...0.82256..
2LIML0 ConvergedINVESTi1.4347922.5908-0.168260.68039......0.07518.-1....
3LIML0 ConvergedLABORw0.767811.5262..0.15132...0.13159..-1.0.43394...


The following statements estimate the model using the 3SLS method. The reduced form estimates are produced by the REDUCED option; IDENTITY statements are used to make the model complete.

proc syslin data=klein 3sls reduced;
   endogenous c p w i x wsum k y;
   instruments klag plag xlag wp g t yr;
   consume: model    c = p plag wsum;
   invest:  model    i = p plag klag;
   labor:   model    w = x xlag yr;
   product: identity x = c + i + g;
   income:  identity y = c + i + g - t;
   profit:  identity p = y - w;
   stock:   identity k = klag + i;
   wage:    identity wsum = w + wp;
run;

The preliminary 2SLS results and estimated cross-model covariance matrix are not shown. The 3SLS estimates are shown in Output 35.1.5 through Output 35.1.7. The reduced form estimates are shown in Output 35.1.8 through Output 35.1.11.

Output 35.1.5: 3SLS Estimates for Consumption

The SYSLIN Procedure
Three-Stage Least Squares Estimation

System Weighted MSE5.9342
Degrees of freedom51
System Weighted R-Square0.9550

ModelCONSUME
Dependent Variablec
LabelConsumption

Parameter Estimates
VariableDFParameter
Estimate
Standard
Error
t ValuePr > |t|Variable
Label
Intercept116.440791.44992511.34<.0001Intercept
p10.1248900.1201791.040.3133Profits
plag10.1631440.1116311.460.1621Profits Lagged
wsum10.7900810.04216618.74<.0001Total Wage Bill


Output 35.1.6: 3SLS Estimates for Investments

ModelINVEST
Dependent Variablei
LabelTaxes

Parameter Estimates
VariableDFParameter
Estimate
Standard
Error
t ValuePr > |t|Variable
Label
Intercept128.177857.5508533.730.0017Intercept
p1-0.013080.179938-0.070.9429Profits
plag10.7557240.1699764.450.0004Profits Lagged
klag1-0.194850.036156-5.39<.0001Capital Stock Lagged


Output 35.1.7: 3SLS Estimates for Labor

ModelLABOR
Dependent Variablew
LabelPrivate Wage Bill

Parameter Estimates
VariableDFParameter
Estimate
Standard
Error
t ValuePr > |t|Variable
Label
Intercept11.7972181.2402031.450.1655Intercept
x10.4004920.03535911.33<.0001Private Production
xlag10.1812910.0379654.780.0002Private Product Lagged
yr10.1496740.0310484.820.0002YEAR-1931


Output 35.1.8: Reduced Form Estimates

Endogenous Variables
 cpwixwsumky
CONSUME1-0.12489000-0.7900800
INVEST00.013079010000
LABOR0010-0.40049000
PRODUCT-100-11000
INCOME-100-10001
PROFIT0110000-1
STOCK000-10010
WAGE00-100100


Output 35.1.9: Reduced Form Estimates

Exogenous Variables
 Interceptplagklagxlagyrgtwp
CONSUME16.440790.163144000000
INVEST28.177850.755724-0.1948500000
LABOR1.797218000.1812910.149674000
PRODUCT00000100
INCOME000001-10
PROFIT00000000
STOCK00100000
WAGE00000001


Output 35.1.10: Reduced Form Estimates

Inverse Endogenous Variables
 CONSUMEINVESTLABORPRODUCTINCOMEPROFITSTOCKWAGE
c1.6346540.6346541.0956570.4388020.1958520.19585201.291509
p0.9723640.972364-0.34048-0.136361.1087211.10872100.768246
w0.6495720.6495721.4405850.5769430.0726290.07262900.513215
i-0.012720.9872820.0044530.001783-0.0145-0.01450-0.01005
x1.6219361.6219361.100111.4405850.1813510.18135101.281461
wsum0.6495720.6495721.4405850.5769430.0726290.07262901.513215
k-0.012720.9872820.0044530.001783-0.0145-0.01451-0.01005
y1.6219361.6219361.100110.4405851.1813510.18135101.281461


Output 35.1.11: Reduced Form Estimates

Reduced Form
 Interceptplagklagxlagyrgtwp
c46.72730.746307-0.123660.1986330.1639910.634654-0.195851.291509
p42.773630.893474-0.18946-0.06173-0.050960.972364-1.108720.768246
w31.572070.596871-0.126570.2611650.2156180.649572-0.072630.513215
i27.61840.744038-0.192370.0008070.000667-0.012720.014501-0.01005
x74.34571.490345-0.316030.199440.1646581.621936-0.181351.281461
wsum31.572070.596871-0.126570.2611650.2156180.649572-0.072631.513215
k27.61840.7440380.807630.0008070.000667-0.012720.014501-0.01005
y74.34571.490345-0.316030.199440.1646581.621936-1.181351.281461


Last updated: June 19, 2025