|The VARMAX Procedure|
|Parameter Estimation and Testing on Restrictions|
In the previous example, the VARX(1,0) model is written as
In Figure 32.20 of the preceding section, you can see several insignificant parameters. For example, the coefficients XL0_1_2, AR1_1_2, and AR1_3_2 are insignificant.
The following statements restrict the coefficients of for the VARX(1,0) model.
/*--- Models with Restrictions and Tests ---*/ proc varmax data=grunfeld; model y1-y3 = x1 x2 / p=1 print=(estimates); restrict XL(0,1,2)=0, AR(1,1,2)=0, AR(1,3,2)=0; run;
The output in Figure 32.21 shows that three parameters , , and are replaced by the restricted values, zeros. In the schematic representation of parameter estimates, the three restricted parameters , , and are replaced by .
|+ is > 2*std error, - is < -2*std error, . is between, * is N/A|
The output in Figure 32.22 shows the estimates of the Lagrangian parameters and their significance. Based on the -values associated with the Lagrangian parameters, you cannot reject the null hypotheses , , and with the 0.05 significance level.
proc varmax data=grunfeld; model y1-y3 = x1 x2 / p=1; test AR(1,3,1)=0; test XL(0,1,2)=0, AR(1,1,2)=0, AR(1,3,2)=0; run;
The output in Figure 32.23 shows that the first column in the output is the index corresponding to each TEST statement. You can reject the hypothesis test at the 0.05 significance level, but you cannot reject the joint hypothesis test at the 0.05 significance level.
|Testing of the Parameters|
|Test||DF||Chi-Square||Pr > ChiSq|
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