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On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models

  • Rachida Ouysse

    ()

In the context of the continuously updated generalized-methods-of-moments (GMM), this study evaluates the finite sample properties of Wald- and criterion-based bootstrap inference for a class of models defined by non-linear conditional moment functions. This work provides simulation evidence that validates the moving block-bootstrap (MBB) as an alternative to asymptotic approximation for robust finite sample GMM inference. The study considers data generating processes with highly non-linear conditional moment functions, weak instruments, and near failure of the identification condition. In the absence of a consensus on best practice when identification is weak, Monte Carlo results of this study are encouraging to the empirical researchers. For criterion-based tests, the MBB performs fairly well in reducing the error in the rejection frequency that occurs when first-order asymptotic critical values are used. In particular, it is possible to improve finite sample inference by inverting bootstrap Wald-type statistics which are commonly used in practice The bootstrap percentile- $$t$$ t confidence intervals performed better than the asymptotic confidence intervals but only marginally in weakly identified specifications with high non-linear moment functions. Copyright Springer-Verlag Berlin Heidelberg 2014

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Article provided by Springer in its journal Computational Statistics.

Volume (Year): 29 (2014)
Issue (Month): 1 (February)
Pages: 233-261

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Handle: RePEc:spr:compst:v:29:y:2014:i:1:p:233-261
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