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Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework

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  • Hwang, Jungbin
  • Sun, Yixiao

Abstract

According to conventional asymptotic theory, the two-step generalized method of moments (GMM) estimator and test perform at least as well as the one-step estimator and test in large samples. The conventional asymptotic theory completely ignores the estimation uncertainty in the weighting matrix, and as a result it may not reflect finite-sample situations well. In this paper, we employ the more accurate fixed-smoothing asymptotic framework to compare the performances of the one-step and two-step procedures. We show that the two-step procedures outperform the one-step procedures only when the squared long-run canonical correlation coefficients between two blocks of transformed moment conditions are larger than the thresholds established in this paper. The thresholds depend on the criteria of interest. A Monte Carlo study lends support to our asymptotic results.

Suggested Citation

  • Hwang, Jungbin & Sun, Yixiao, 2018. "Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework," Journal of Econometrics, Elsevier, vol. 207(2), pages 381-405.
    • Handle: RePEc:eee:econom:v:207:y:2018:i:2:p:381-405
    DOI: 10.1016/j.jeconom.2018.07.006
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    References listed on IDEAS

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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