Silvapulle and Sen (2004) propose a test statistic for testing hypotheses where the null or the alternative hypothesis or both involve inequalities. You can test special cases of these hypotheses with the JOINT option in the ESTIMATE and the LSMESTIMATE statement. Consider the k estimable functions and the hypotheses and . The alternative hypothesis defines a convex cone at the origin. Suppose that under the null hypothesis follows a multivariate normal distribution with mean and variance . The restricted alternative prevents you from using the usual F or chisquare test machinery, since the distribution of the test statistic under the alternative might not follow the usual rules. Silvapulle and Sen (2004) coined a statistic that takes into account the projection of the observed estimate onto the convex cone formed by the alternative parameter space. This test statistic is called the chibarsquare statistic, and pvalues are obtained by simulation; see, in particular, Chapter 3.4 in Silvapulle and Sen (2004).
Briefly, let be a multivariate normal random variable with mean and variance matrix . The chibarsquare statistic is the random variable






and it can be motivated by a geometric argument. The quadratic form in Q is the projection of onto the cone . Suppose that this projected point is . If , then Q = 0 and . If is completely outside of the cone , then is a point on the surface of the cone. Similarly, is the length of the segment from the origin to in the space with norm . If you apply the Pythagorean theorem, you can see that the chibarsquare statistic measures the length of the segment from the origin to the projected point in .
To calculate pvalues for chibarsquare statistics, a simulationbased approach is taken. Consider again the set of k estimable functions with estimate and variance .
First, the observed value of the statistic is computed as
Then, n independent random samples are drawn from an distribution and the following chibarstatistics are computed for the sample:









The pvalue is estimated by the fraction of simulated statistics that are greater than or equal to the observed value .
Notice that unless is interior to the cone , finding the value of Q requires the solution to a quadratic optimization problem. When k is large, or when many simulations are requested, the computation of pvalues for chibarsquare statistics might require considerable computing time.