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• W2022974825 abstract "In this text, we review recent developments in econometrics from the view-point of statistical test theory. We first review some basic principles of philosophy of science and statistical theory, emphasizng parsimony and falsifiability as criteria for evaluating models, test theory as a formalization of the falsification principle for probabilistic models, and the logical foundation of basic notions in test theory (such as the level of a test). We then show that some of the most frequently used statistical and econometric methods are fundamentally inappropriate for the problems and models considered, while several hypotheses, for which test procedures are commonly proposed, are not testable at all. Such situations lead to ill-defined statistical problems. We analyze several cases of such problems: (1) building confidence intervals in structural models where identification problems may be present; (2) the construction of tests for nonparametric hypotheses, including procedures robust to heteroskadasticity, non-normality or dynamic specification. We point out that these difficulties often originate from the ambition to weaken regularity conditions typically required by statistical analysis, and from an inappropriate use of asymptotic distributional theory. Finally, we underscore the importance of formulating testable hypotheses and models, and of developing econometric methods with provable finite-sample properties. Dans ce texte, nous analysons les developpements recents de l’econometrie a la lumiere de la theorie des tests statistiques. Nous revoyons d’abord quelques principes fondamentaux de philosophie des sciences et de theorie statistique, en mettant l’accent sur la parcimonie et la falsifiabilite comme criteres d’evaluation des modeles, sur le role de la theorie des tests comme formalisation du principe de falsification de modeles probabilistes, ainsi que sur la justification logique des notions de base de la theorie des tests (telles que le niveau d’un test). Nous montrons ensuite que certaines des methodes statistiques et econometriques les plus utilisees sont fondamentalement inappropriees pour les problemes et modeles consideres, tandis que de nombreuses hypotheses, pour lesquelles des procedures de test sont communement proposees, ne sont en fait pas du tout testables. De telles situations conduisent a des problemes statistiques mal poses. Nous analysons quelques cas particuliers de tels problemes : (1) la construction d’intervalles de confiance dans le cadre de modeles structurels qui posent des problemes d’identification; (2) la construction de tests pour des hypotheses non parametriques, incluant la construction de procedures robustes a l’heteroscedasticite, a la non-normalite ou a la specification dynamique. Nous indiquons que ces difficultes proviennent souvent de l’ambition d’affaiblir les conditions de regularite necessaires a toute analyse statistique ainsi que d’une utilisation inappropriee de resultats de theorie distributionnelle asymptotique. Enfin, nous soulignons l’importance de formuler des hypotheses et modeles testables, et de proposer des techniques econometriques dont les proprietes sont demontrables dans les echantillons finis." @default.
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• W2022974825 date "2001-01-01" @default.
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• W2022974825 title "Logique et tests d’hypothèses" @default.
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