1
Download EViews 4.0 User's Guide
Transcript
482—Chapter 18. The Log Likelihood (LogL) Object LogL: MLOGIT Method: Maximum Likelihood (Marquardt) Date: 10/19/00 Time: 14:26 Sample: 1 1000 Included observations: 1000 Evaluation order: By observation Estimation settings: tol= 1.0E-09 Initial Values: B2(1)=-1.08356, B2(2)=0.90467, B2(3)=-0.06786, B3(1)= -0.69842, B3(2)=-0.33212, B3(3)=0.32981 Convergence achieved after 7 iterations B2(1) B2(2) B2(3) B3(1) B3(2) B3(3) Log likelihood Avg. log likelihood Number of Coefs. Coefficient Std. Error z-Statistic Prob. -0.521793 0.994358 0.134983 -0.262307 0.176770 0.399166 0.205568 0.267963 0.265655 0.207174 0.274756 0.274056 -2.538302 3.710798 0.508115 -1.266122 0.643371 1.456511 0.0111 0.0002 0.6114 0.2055 0.5200 0.1453 -1089.415 -1.089415 6 Akaike info criterion Schwarz criterion Hannan-Quinn criter. 2.190830 2.220277 2.202022 EViews also provides the log likelihood value, average log likelihood value, number of coefficients, and three Information Criteria. By default, the starting values are not displayed. Here we have used the Estimation Options dialog to instruct EViews to display the estimation starting values in the output. Gradients The gradient summary, table and graph view allow you to examine the gradients of the likelihood. These gradients are computed at the current parameter values (if the model has not yet been estimated), or at the converged parameter values (if the model has been estimated). See Appendix E, “Gradients and Derivatives”, on page 675 for additional details. You may find this view to be a useful diagnostic tool when experiencing problems with convergence or singularity. One common problem leading to singular matrices is a zero derivative for a parameter due to an incorrectly specified likelihood, poor starting values, or a lack of model identification. See the discussion below for further details.
