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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

Jungbin Hwang and Yixiao Sun ()

Journal of Econometrics, 2018, vol. 207, issue 2, 381-405

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.

Keywords: Asymptotic mixed normality; Fixed-smoothing asymptotics; Heteroskedasticity and autocorrelation; Nonstandard asymptotics; Two-step GMM estimation (search for similar items in EconPapers)
JEL-codes: C12 C32 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Should We Go One Step Further? An Accurate Comparison of One-step and Two-step Procedures in a Generalized Method of Moments Framework (2015) Downloads
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