ESTIMATION AND INFERENCE WITH WEAK, SEMI-STRONG, AND STRONG IDENTIFICATION
By Donald W. K. Andrews, Xu Cheng. 2010
190 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/517231e55828cc38a6c822b7d71d992fb5bd022c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- D. Feir
- S. Freyaldenhoven
- Vadim Marmer
- Elie Tamer
- Xiaohong Chen
- Thomas Lemieux
- Alexander Torgovitsky
- Isaiah Andrews
In Statistics and Probability
- Elie Tamer
- Alexander Torgovitsky
- Xiaohong Chen
- D. Feir
- S. Freyaldenhoven
- Vadim Marmer
- Thomas Lemieux
- Isaiah Andrews
Citations in the corpus
- Conditional Linear Combination Tests for Weakly Identified Models (2016, background): “Statistics asymptotically equivalent to (8) for appropriately defined gT have been 2Other examples may be found in Olea (2012), who shows that for appropriately defined gT (θ0) and ∆gT (θ) convergence of the form (2) holds in several weakly identified extremum estimation examples, including a probit”
- Weak Identification in Fuzzy Regression Discontinuity Designs (2016, methodology): “This idea can be formalized using the results obtained in the recent literature on uniform size properties of tests and confidence sets: Andrews and Guggenberger (2010), Andrews and Cheng (2012), and Andrews, Cheng, and Guggenberger (2011). The latter article provides a general framework of establis”
- Sensitivity Analysis in Semiparametric Likelihood Models (2011, methodology): “Andrews and Cheng (2010) provide methods for constructing confidence regions and tests including parametric likelihood models that are robust to lack of or weak identification. The sieve literature for semiparametric models under point identification is exposited in Chen (2007).(3) In particular, Mu”
- Factor Models with Local Factors — Determining the Number of Relevant Factors (2021, methodology): “A similar framework is considered in Bailey et al. (2020) and Uematsu and Yamagata (2020). Onatski (2009, 2010, 2012) proposes an alternative framework for weak factors through random matrix theory, and a similar model to the one used in this paper has been considered in the large body of literature”