Instrumental variable estimation in the presence of many moment conditions
By Ryo Okui. 2011
70 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/9d0dd1ede9f9e5a84404a859cd0fc472554736fd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Victor Chernozhukov
- Christian Hansen
- Alexandre Belloni
- Kengo Kato
- D. Chetverikov
- Damian Kozbur
- Xu Cheng
- Zhipeng Liao
In Finance
- Christian Hansen
- Damian Kozbur
- M. Carrasco
- Victor Chernozhukov
- Alexandre Belloni
- Xu Cheng
- Zhipeng Liao
- Ryo Okui
Citations in the corpus
- High-dimensional econometrics and regularized GMM (2018, background): “See, for example, [41], [100], [37], and [71] for approaches that explicitly use regularized first-stage estimation within a dense model framework.”
- The optimal choice of moments in dynamic panel data models (2009, methodology): “The second method is based on the “shrinkage GMM” approach developed by Okui (2004) for cross-sectional IV regressions.”
- Instrumental variables estimation with many weak instruments using regularized JIVE (2014, methodology): “Okui (2010) derives the asymptotic distribution of the resulting IV estimator and provides a method for choosing the ridge regression smoothing parameter that minimizes the higher-order asymptotic mean-squared-error (MSE) of the IV estimator.”
- Massachusetts Institute of Technology Department of Economics Working Paper Series Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain (unknown): “[2] considers IV estimation with many instruments based on principal components analysis and variable selection via boosting, and [39] provides results for Ridge regression.”
- A regularization approach to the many instruments problem (2012, methodology): “In an attempt to improve the properties of the generalized method of moments (GMM) in presence of many moments, Kuersteiner (2002, 2006) proposes a kernel weighted GMM estimator and Okui (2004) introduces a shrinkage parameter to allocate less weight on a subset of instruments.”
- Select the Valid and Relevant Moments: An Information-Based LASSO for GMM with Many Moments (2013, methodology): “Shrinkage estimation for homoskedastic linear IV models is considered by Chamberlain and Imbens (2004) and Okui (2011).”