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Hi,

I have estimated an equation with the following results:

Variable | Coefficient | Std. Error | t-Statistic | Prob.

C(1) | 0.125698 | 0.157044 | 0.800397 | 0.423700
C(2) | 0.765549 | 0.851063 | 0.899522 | 0.368700
C(3) |-0.163058 | 0.320517 |-0.508735 | 0.611100
C(4) |-0.003991 | 0.106239 |-0.037570 | 0.970000
C(5) |-0.464766 | 0.245652 |-1.891971 | 0.058900
C(6) | 8.757433 | 10.08753 | 0.868144 | 0.385600

To me, this means that all of the variables are effectively zero except for variable 5.

I want to test the hyposthesis that all of the variables are zero.
So, when I perform a Wald test with the restriction c(1)=c(2)=c(3)=c(4)=c(5)=c(6)=0, I get a p-value from the F-test of 0.064 (as expected)

However, if I exclude variable 5 from the Wald test restriction (ie. test the statment c(1)=c(2)=c(3)=c(4)=c(6)=0) I get a p-value of 0.05

Basically, by excluding each variable in turn I discovered that it is variable 4 (the variable which seems closest to zero in the estimation) which is causing the rejection of the null-hypothesis in the Wald test.

Can somebody with better stats knowledge than me explain why this is happening?

You probably have substantial multicollinearity. The regression knows that the variables collectively matter, but can't separately tell which one(s) are really nonzero.

Thanks for replying.

So I shouldn't be drawing any conclusions from the individual t-statistics, rather, I should only trust the 'collective' results of the Wald test?

Thanks for replying.

So I shouldn't be drawing any conclusions from the individual t-statistics, rather, I should only trust the 'collective' results of the Wald test?

That's probably right. At least if the variables you have are all measures related to some common phenomenon.

One of those cases where "Non-normality and collinearity are problems!"