An extension of a standard test for heteroskedasticity to a systems framework
Опубликовано на портале: 07-04-2004
Journal of Econometrics. 1982. Vol. 20. No. 2. P. 325-333.
In recent papers Amemiya (1977) and White (1980) gave results which justify the large sample version of the Glejser test based on squared estimated disturbances. However, their results assumed the absence of lagged dependent variables, and were given in the single-equation context. The purpose of this paper is to extend the Amemiya (1977) and White (1980) results to the case of a simultaneous equation framework, which may or may not contain lagged endogenous variables. We consider two cases. The first is the one in which the researcher suspects that heteroskedasticity may exist in only one of the system’s equations. This extension is not trivial due to, among other things, feedbacks involving the endogenous regressors. Nevertheless, it turns out that if the least-squares procedure in the first stage is replaced by virtually any consistent procedure, such as two-stage least- squares - henceforth 2SLS, no additional complexities arise. The importance of this result is that under typical modelling specifications, a computationally simple large sample test for heteroskedasticity, which is associated with one equation of a system, can be carried out in the context of that system. This test should be especially useful in those cases in which the exact specification of the ‘suspected’ heteroskedasticity is not known1 The second case we consider is the one in which the researcher suspects that heteroskedasticity may be associated with more than one equation of the system. As expected, the resulting test is computationally more ‘demanding’.
The model is specified in section 2, and the basic results are given in section 3. Suggestions for further work are given in section 4; technical details are relegated to the appendix.
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International Economic Review. 1987. Vol. 28. No. 3. P. 777-787.
Сайт Института математики и статистики (Institute of Mathematics, Statistics and Actuarial Science, University of Kent at Canterbury)