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...

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Bibliographic Details
Published in:	Journal of Econometrics Vol. 133; no. 2; pp. 479 - 513
Main Authors:	Jouneau-Sion, Frédéric, Torrès, Olivier
Format:	Article
Published:	Elsevier Science August 2006
Subjects:	Monte Carlo method Algorithms Probability theory
Online Access:	View this record in EBSCOhost

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Summary:	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.