MMC techniques for limited dependent variables models: Implementation by the branch-and-bound algorithm.
We propose a finite sample approach to some of the most common limited dependent variables models. The method rests on the maximized Monte Carlo (MMC) test technique proposed by Dufour [1998. Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard as...
| Publicado en: | Journal of Econometrics Vol. 133; no. 2; pp. 479 - 513 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Elsevier Science
August 2006
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | We propose a finite sample approach to some of the most common limited dependent variables models. The method rests on the maximized Monte Carlo (MMC) test technique proposed by Dufour [1998. Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics. Journal of Econometrics, this issue]. We provide a general way for implementing tests and confidence regions. We show that the decision rule associated with a MMC test may be written as a Mixed Integer Programming problem. The branch-and-bound algorithm yields a global maximum in finite time. An appropriate choice of the statistic yields a consistent test, while fulfilling the level constraint for any sample size. The technique is illustrated with numerical data for the logit model. Copyright (c) 2006 Elsevier B.V. |
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