Two score tests for heteroskedasticity in the errors of a bivariate Probit model are
developed, and numerous simulations are performed. These tests are based on an outer
product of the gradient estimate of the information matrix, and are constructed using an
artificial regression. The empirical sizes of both tests are found to be well-behaved,
settling down to the nominal size under the asymptotic distribution as the sample size
approaches 1000 observations. Similarly, the empirical powers of both tests increase
quickly with sample size. The largest improvement in power occurs as the sample size
increases from 250 to 500. An application with health care data from the German
Socioeconomic Panel is performed, and strong evidence of heteroskedasticity is detected.
This suggests that the maximum likelihood estimator for the standard bivariate Probit
model will be inconsistent in this particular case. / Graduate / 0501
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4670 |
| Date | 28 June 2013 |
| Creators | Thorn, Thomas |
| Contributors | Giles, David E. A. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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