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Assessing the magnitude of the concentration parameter in a simultaneous equations model

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Abstract

This paper provides the practitioner with a method of ascertaining when the concentration parameter in a simultaneous equations model is small. We provide some exact distribution theory for a proposed statistic and show that the statistic possesses the minimal desirable characteristics of a test statistic when used to test that the concentration parameter is zero. The discussion is then extended to consider how to test for weak instruments using this statistic as a basis for inference. We also discuss the statistic's relationship to various other procedures that have appeared in the literature. Copyright The Author(s). Journal compilation Royal Economic Society 2009

Suggested Citation

  • D. S. Poskitt & C. L. Skeels, 2009. "Assessing the magnitude of the concentration parameter in a simultaneous equations model," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 26-44, March.
    • Handle: RePEc:ect:emjrnl:v:12:y:2009:i:1:p:26-44
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    Cited by:

    1. Vivienne Pham & David Prentice, 2010. "An empirical Analysis of the Counter-factual: A Merger and Divestiture in the Australian Cigarette Industry," Working Papers 2010.08, School of Economics, La Trobe University.
    2. Matthew C. Harding & Jerry Hausman & Christopher Palmer, 2015. "Finite sample bias corrected IV estimation for weak and many instruments," CeMMAP working papers CWP41/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Poskitt, D.S. & Skeels, C.L., 2007. "Approximating the distribution of the two-stage least squares estimator when the concentration parameter is small," Journal of Econometrics, Elsevier, vol. 139(1), pages 217-236, July.
    4. Tchatoka, Firmin Doko, 2015. "Subset Hypotheses Testing And Instrument Exclusion In The Linear Iv Regression," Econometric Theory, Cambridge University Press, vol. 31(6), pages 1192-1228, December.
    5. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "The First-stage F Test with Many Weak Instruments," Papers 2302.14423, arXiv.org, revised Sep 2024.
    6. Don S. Poskitt, 2020. "On GMM Inference: Partial Identification, Identification Strength, and Non-Standard," Monash Econometrics and Business Statistics Working Papers 40/20, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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