# Significance of individual coefficients vs Significance of both

This was a question I read from google quantitative analyst interview on glassdoor: If each of the two coefficient estimates in a regression model is statistically significant, do you expect the test of both together is still significant?

I thought it was an interesting question, and I was wondering if there are counterexamples or there is a way to prove that it's always the case?

Any suggestion or hint would be greatly appreciated.

• How many variables are in the model in total? Are you just interested in the significance of the nested model test that drops both, or do you care about anything else (whether the model as a whole is significant)? @ChristophHanck, I think that is a different case. – gung - Reinstate Monica May 8 '15 at 12:09
• "each of the two" sounds like two in total to me. – Christoph Hanck May 8 '15 at 12:11

Consider the Wald statistic, which resembles the familiar F-statistic $F$ (we use the default version that is not robust to heteroskedasticity): \begin{align*} W&=n(Rb-u)'\left[R\left[n\cdot s^2\cdot(X'X)^{-1}\right]R'\right]^{-1}(Rb-u)\notag\\ &=(Rb-u)'\left[R(X'X)^{-1}R'\right]^{-1}(Rb-u)/s^2\\ &=J\cdot F\notag, \end{align*} where $J$ gives the number of restrictions tested, with $H_0: R\beta=u$. If you want to test if neither of the variables enters the model, you simply take $R=I$, the identity matrix, and $u=(0,0)^T$.

Let us now find the non-rejection region of the Wald test as a function of the parameter vector $\beta$ (so the set of hypotheses you would not reject given a certain statistic computed from the data). $H_{0}$ is to be rejected at level $\alpha$ if $$W>\chi^{2}(J,1-\alpha),$$ the $1-\alpha$-quantile the $\chi^{2}$-distribution with $J$ degrees of freedom. The acceptance region thus corresponds to the values $$\theta=R\beta$$ for which $H_0$ would not have been rejected at level $\alpha$, $$\{\theta:W\leq\chi^{2}(J,1-\alpha)\}$$

To visualize, consider the case $J=2$. Then, $\chi^{2}(2,0.95)=5.99$ for $\alpha=0.05$ and $\chi^{2}(2,0.99)=9.21$ for $\alpha=0.01$. Write $T=Rb$ (with $b$ the OLS estimator for the two coefficients) and $z=\theta-T$. Further, to abbreviate the algebra, summarize the inverse matrix as $$R\left[n\cdot s^2\cdot(X'X)^{-1}\right]R'=:V:=\left( \begin{array}{cc} 1 & r \\ r & a \\ \end{array} \right),$$ where $|r|<\sqrt{a}$ to ensure invertibility of $V$. We further have $$V^{-1}=\frac{1}{a-r^2}\cdot\left( \begin{array}{cc} a & -r \\ -r & 1 \\ \end{array} \right),$$ and $W=z'V^{-1}z$ or $$W=(az_1^2+z_2^2-2\,r\,z_1 z_2)/(a-r^2)\qquad\qquad(*)$$ We hence now consider $W$ as a function of the hypothesized coefficients $\theta$.

The result for $T=0$ (so an OLS estimate of $(0,0)^T$), $r=0.6,\,a=1$ (see below for the code): The dashed lines indicate the acceptance regions $[-1.96,1.96]$ that you get if you test each coefficient separately. The rectangle formed by the two intervals gives you the region where neither t-test rejects. The ellipses give you the regions of pairs of parameter values for which you would not have rejected the null at either 5 or 1%.

So, here is the answer: you see that there is small lightblue region outside the rectangle but inside the 5%-acceptance region of the Wald test, i.e., a region where both individual t-tests would have rejected but the joint test would not. So, yes, there are counterexamples, which as indicated by the example are however not expected to occur frequently.

EDIT: To follow up on the point made by @whuber here is the corresponding figure for the case $r=0$, i.e. no correlation. r <- 0.6 # set to zero for uncorrelated case
a <- 1
W <- function(beta1,beta2,a,r) (a*beta1^2+beta2^2−2*r*beta1*beta2)/(1−r^2)

alpha <- 0.05
beta1 <- beta2 <- seq(-3,3,0.01)
z <- outer(beta1,beta2,W,a=a,r=r)

normcv <- qnorm(1-alpha/2)
contour(beta1,beta2,z,levels=qchisq(1-alpha,2))
abline(h=-normcv,lty=2)
abline(h=normcv,lty=2)
abline(v=-normcv,lty=2)
abline(v=normcv,lty=2)

z.nonrej <- z<=qchisq(1-alpha,2)
beta1.nw <- beta1 >= normcv
beta2.nw <- beta2 >= normcv
beta.nw <- outer(beta1.nw,beta2.nw,"+")==2
nw.nonrejection.Wald <- (z.nonrej + beta.nw)==2

ind.nw <- which(nw.nonrejection.Wald==T, arr.ind = T)
points(beta1[ind.nw[,1]],beta2[ind.nw[,2]], col="lightblue", cex=.1)

beta1.se <- beta1 <= -normcv
beta2.se <- beta2 <= -normcv
beta.se <- outer(beta1.se,beta2.se,"+")==2
se.nonrejection.Wald <- (z.nonrej + beta.se)==2

ind.se <- which(se.nonrejection.Wald==T, arr.ind = T)
points(beta1[ind.se[,1]],beta2[ind.se[,2]], col="lightblue", pch='.')


The figure shows that producing the counterexample indeed required allowing for correlation among the estimates.

EDIT 2:

In response to Kevin Kim's question in the comments:

Interestingly, the fact that it is possible that neither individual test rejects but that the Wald test does when there is no correlation is not a general result for any significance level $\alpha$. When choosing a sufficiently high significance level $\alpha$ of beyond roughly $\alpha\approx0.2151$, the ball covers the entire rectangle.

Basically, consider the function of the circle of the acceptance border of the Wald test, i.e. $(*)$ for $a=1$ and $r=0$ set equal to $\chi^{2}(2,1-\alpha)$ and solving for $z_2$ (focusing on the positive quadrant w.l.o.g.): $$z_2(z_1)=\sqrt{\chi^{2}(2,1-\alpha)-z_1^2}$$ We now seek the value for $\alpha$ for which the function evaluated at the normal quantile is just the normal quantile, or $$\sqrt{\chi^{2}(2,1-\alpha)-\Phi^{-1}(1-\alpha/2)^2}=\Phi^{-1}(1-\alpha/2),$$ i.e., where the curve is equal to the corner of the rectangle.

Doing this numerically in R gives

rootfunc <- function(alpha) sqrt(qchisq(1-alpha,2) - qnorm(1-alpha/2)^2) - qnorm(1-alpha/2)
uniroot(rootfunc,interval = c(0.00001,0.9999))


with solution

$root  0.2151346  So indeed, the ball seems to shrink more slowly than the rectangle. • +1 very nicely done! There appear to be two effects here. First, any correlation among coefficient estimates indicates that certain combinations of relatively small individual variations are unlikely, such as a low value for one and a high value for another as shown in the figure. Second, even in the absence of correlation the F test is contemplating spherical combinations of estimates while the individual tests are looking only at individual estimates. The resulting geometric discrepancy between spheres and boxes can produce counterexamples. – whuber May 8 '15 at 15:57 • @Glen_b, actually, thank you for the pointer. I now highlight in colour the region I was referring to in my answer, which hopefully makes it clearer. And if someone feels like suggesting a smarter way of coding this, please feel free - I have the impression that my approach is rather clumsy. – Christoph Hanck Dec 5 '16 at 9:44 • @ChristophHanck If we change the significant level but keep$r=0$, i.e., no correlation, then both the oval and the rectangle will change accordingly. I wonder if one could actually show that the "lightblue" region will never appear in the case of$r=0$? Is it possible that if you increase$\alpha$, then the rectangle shrinks at a faster rate than the oval, so that the "lightblue" region will show up again? – KevinKim Dec 12 '16 at 3:13 • @KevinKim, interestingly, the answer is indeed yes, see my edit for the cutoff$\alpha\approx0.215\$. – Christoph Hanck Dec 12 '16 at 11:25