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An Expository Note on the Existence of Moments of Fuller and HFUL Estimators

In: Essays in Honor of Jerry Hausman

Author

Listed:
  • John C. Chao
  • Jerry A. Hausman
  • Whitney K. Newey
  • Norman R. Swanson
  • Tiemen Woutersen

Abstract

In a recent paper, Hausman, Newey, Woutersen, Chao, and Swanson (2012) propose a new estimator, HFUL (Heteroscedasticity robust Fuller), for the linear model with endogeneity. This estimator is consistent and asymptotically normally distributed in the many instruments and many weak instruments asymptotics. Moreover, this estimator has moments, just like the estimator by Fuller (1977). The purpose of this note is to discuss at greater length the existence of moments result given in Hausman et al. (2012). In particular, we intend to answer the following questions: Why does LIML not have moments? Why does the Fuller modification lead to estimators with moments? Is normality required for the Fuller estimator to have moments? Why do we need a condition such as Hausman et al. (2012), Assumption 9? Why do we have the adjustment formula?

Suggested Citation

  • John C. Chao & Jerry A. Hausman & Whitney K. Newey & Norman R. Swanson & Tiemen Woutersen, 2012. "An Expository Note on the Existence of Moments of Fuller and HFUL Estimators," Advances in Econometrics, in: Essays in Honor of Jerry Hausman, pages 87-106, Emerald Group Publishing Limited.
  • Handle: RePEc:eme:aecozz:s0731-9053(2012)0000029009
    DOI: 10.1108/S0731-9053(2012)0000029009
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    Cited by:

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    2. Stuart Lane, 2025. "The moment is here: a generalised class of estimators for fuzzy regression discontinuity designs," Papers 2511.03424, arXiv.org.

    More about this item

    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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