Double robust inference for continuous updating GMM

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Tác giả: Frank Kleibergen, Zhaoguo Zhan

Ngôn ngữ: eng

Ký hiệu phân loại: 003.76 Stochastic systems

Thông tin xuất bản: 2021

Mô tả vật lý:

Bộ sưu tập: Báo, Tạp chí

ID: 166984

 Comment: 77 Pages, 25 figuresWe propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the pseudo-true value of the structural parameters in the generalized method of moments. The pseudo-true value is defined as the minimizer of the population continuous updating objective function and equals the true value of the structural parameter in the absence of misspecification.\nocite{hhy96} The (bounding) chi-squared limiting distribution of the DRLM statistic is robust to both misspecification and weak identification of the structural parameters, hence its name. To emphasize its importance for applied work, we use the DRLM test to analyze the return on education, which is often perceived to be weakly identified, using data from Card (1995) where misspecification occurs in case of treatment heterogeneity
  and to analyze the risk premia associated with risk factors proposed in Adrian et al. (2014) and He et al. (2017), where both misspecification and weak identification need to be addressed.
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