The SYSLIN Procedure |

Testing Parameters |

You can test linear hypotheses about the model parameters with TEST and STEST statements. The TEST statement tests hypotheses about parameters in the equation specified by the preceding MODEL statement. The STEST statement tests hypotheses that relate parameters in different models.

For example, the following statements test the hypothesis that the price coefficient in the demand equation is equal to 0.015.

/*--- 3SLS Estimation with Tests ---*/ proc syslin data=in 3sls; endogenous p; instruments y u s; demand: model q = p y s; test_1: test p = .015; supply: model q = p u; run;

The TEST statement results are shown in Figure 27.10. This reports an *F* test for the hypothesis specified by the TEST statement. In this case, the *F* statistic is 6.79 (3.879/.571) with 1 and 113 degrees of freedom. The *p* value for this *F* statistic is 0.0104, which indicates that the hypothesis tested is almost but not quite rejected at the 0.01 level. See the section TEST Statement for details.

System Weighted MSE | 0.5711 |
---|---|

Degrees of freedom | 113 |

System Weighted R-Square | 0.9627 |

To test hypotheses that involve parameters in different equations, use the STEST statement. Specify the parameters in the linear hypothesis as *model-label.regressor-name.* (If the MODEL statement does not have a label, you can use the dependent variable name as the label for the model, provided the dependent variable uniquely labels the model.)

For example, the following statements test the hypothesis that the income coefficient in the demand equation is 0.01 times the unit cost coefficient in the supply equation:

proc syslin data=in 3sls; endogenous p; instruments y u s; demand: model q = p y s; supply: model q = p u; stest1: stest demand.y = .01 * supply.u; run;

The STEST statement results are shown in Figure 27.11. The form and interpretation of the STEST statement results are like the TEST statement results. In this case, the *F* test produces a *p* value less than 0.0001, and strongly rejects the hypothesis tested. See the section STEST Statement for details.

System Weighted MSE | 0.5711 |
---|---|

Degrees of freedom | 113 |

System Weighted R-Square | 0.9627 |

Parameter Estimates | ||||||
---|---|---|---|---|---|---|

Variable | DF | Parameter Estimate |
Standard Error | t Value | Pr > |t| | Variable Label |

Intercept | 1 | 1.980269 | 1.169176 | 1.69 | 0.0959 | Intercept |

p | 1 | -1.17654 | 0.605015 | -1.94 | 0.0568 | Price |

y | 1 | 0.404117 | 0.117179 | 3.45 | 0.0011 | Income |

s | 1 | 0.359204 | 0.085077 | 4.22 | <.0001 | Price of Substitutes |

Parameter Estimates | ||||||
---|---|---|---|---|---|---|

Variable | DF | Parameter Estimate |
Standard Error | t Value | Pr > |t| | Variable Label |

Intercept | 1 | -0.51878 | 0.490999 | -1.06 | 0.2952 | Intercept |

p | 1 | 1.333080 | 0.059271 | 22.49 | <.0001 | Price |

u | 1 | -1.14623 | 0.243491 | -4.71 | <.0001 | Unit Cost |

You can combine TEST and STEST statements with RESTRICT and SRESTRICT statements to perform hypothesis tests for restricted models. Of course, the validity of the TEST and STEST statement results depends on the correctness of any restrictions you impose on the estimates.

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