[U] User's Guide

280 [ U ] 20 Estimation and postestimation commands. ologit rep78 price foreign weight weightsq displ, nologOrdered logistic regression Number of obs = 69LR chi2(5) = 33.12Prob > chi2 = 0.0000Log likelihood = -77.133082 Pseudo R2 = 0.1767rep78 Coef. Std. Err. z P>|z| [95% Conf. Interval]price -.000034 .0001188 -0.29 0.775 -.0002669 .000199foreign 2.685648 .9320398 2.88 0.004 .8588833 4.512412weight -.0037447 .0025609 -1.46 0.144 -.0087639 .0012745weightsq 7.87e-07 4.50e-07 1.75 0.080 -9.43e-08 1.67e-06displacement -.0108919 .0076805 -1.42 0.156 -.0259455 .0041617/cut1 -9.417196 4.298201 -17.84151 -.9928766/cut2 -7.581864 4.23409 -15.88053 .7168002/cut3 -4.82209 4.147679 -12.95139 3.307212/cut4 -2.79344 4.156219 -10.93948 5.352599We now wonder whether all our variables other than foreign are jointly significant. We test thehypothesis just as we would after linear regression:. test weight weightsq displ price( 1) [rep78]weight = 0( 2) [rep78]weightsq = 0( 3) [rep78]displacement = 0( 4) [rep78]price = 0chi2( 4) = 3.63Prob > chi2 = 0.4590To compare this with the results performed by a likelihood-ratio test, see [U] 20.11.3 Likelihood-ratiotests. Here results differ little.20.11.3 Likelihood-ratio testsAfter maximum likelihood estimation, you can obtain likelihood-ratio tests by fitting both theunconstrained and constrained models, storing the results using estimates store, and then runninglrtest. See [R] lrtest for the full details.Example 14In [U] 20.11.2 Using test above, we fit an ordered logit on rep78 and then tested the significanceof all the explanatory variables except foreign.To obtain the likelihood-ratio test, sometime after fitting the full model, we type estimates storefull model name, where full model name is just a label that we assign to these results.. ologit rep78 price foreign weight weightsq displ(output omitted ). estimates store myfullmodelThis command saves the current model results with the name myfullmodel.