Selecting Instrumental Variables in a Data Rich Environment

By Serena Ng, Jushan Bai. 2009

76 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fcd28f0a36a908c68d454c200ebaccb4538cba59

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Citations in the corpus

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.”
Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain (2012, methodology): “For theoretical and simulation evidence regarding Lasso’s performance, see Bai and Ng (2008, 2009a), Bickel, Ritov, and Tsybakov (2009), Bunea, Tsybakov, and Wegkamp (2006, 2007a, 2007b), Candes and Tao (2007), Huang, Horowitz, and Wei (2010), Knight (2008), Koltchinskii (2009), Lounici (2008),…”
Access to Markets and Rural Poverty: Evidence from Household Consumption in China (2013, methodology): “17 If we use principal components to reduce the dimension of the instruments following the advice of Amemia (1966) and Bai and Ng (2008), the conclusions reported in this paper remain intact.”
Cross-Sectional Dependence in Panel Data Analysis (2012, background): “Some of the issues discussed in the article are also briefly mentioned by Baltagi and Pesaran (2007), which is an introduction to a special issue of the Journal of Applied Econometrics, and Hsiao (2007) in an article reviewing the state of the art and the current issues in panel data analysis.”
Select the Valid and Relevant Moments: An Information-Based LASSO for GMM with Many Moments (2013, methodology): “The boosting method is suggested for IV selection by Bai and Ng (2009).”