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Identification, weak instruments, and statistical inference in econometrics

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  • Jean‐Marie Dufour

Abstract

. We discuss statistical inference problems associated with identification and testability in econometrics. We consider inference in non‐parametric models and weakly identified structural models (weak instruments). We point out that many ill‐defined statistical problems, such as non‐testable hypotheses, occur in these areas and are typically associated with asymptotic approximations. In non‐parametric models, such problems include testing moments and inference under heteroscedasticity or serial dependence of unknown form. For weakly identified structural models, difficulties are typically associated with improper pivots, and we review recent developments aimed at proposing more reliable procedures, including alternative proposed statistics, bounds, projection, split‐sampling, conditioning, Monte Carlo tests. JEL classification: C1, C12, C14, C15, C3, C5 Identification, instruments faibles, et inférence statistique en econométrie. Nous analysons les problèmes d’inférence associés à l’identification et à la testabilité en économétrie. Nous considérons l’inférence dans les modèles non‐paramétriques et les modèles structurels faiblement identifiés (instruments faibles). Nous remarquons que beaucoup de problèmes mal posés, tels que des hypothèses non testables, apparaissent dans ces domaines et que ceux‐ci sont typiquement associés à l’emploi d’approximations asymptotiques. Dans les modèles non‐paramétriques, de tels problèmes incluent les tests sur les moments et l’inférence sous hétéroscédasticité ou dépendance sérielle de forme non spécifiée. Dans les modèles structurels faiblement identifiés, ces difficultés sont habituellement associées à l’emploi de fonctions pivotales impropres et nous présentons un survol des méthodes récentes ayant pour objectif d’obtenir des procédures plus fiables, ce qui comprend les différentes statistiques proposées, l’emploi de bornes, la subdivision d’échantillon, les techniques de projection, le conditionnement et les tests de Monte Carlo.

Suggested Citation

  • Jean‐Marie Dufour, 2003. "Identification, weak instruments, and statistical inference in econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 36(4), pages 767-808, November.
  • Handle: RePEc:wly:canjec:v:36:y:2003:i:4:p:767-808
    DOI: 10.1111/1540-5982.t01-3-00001
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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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