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…

By Alexandre Belloni, Daniel L. Chen, Victor Chernozhukov, Christian Hansen

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “(2003); Belloni et al. (2014)) or high-dimensional central limit theorems (Chernozhukov et al.”
Post-Selection Inference for Generalized Linear Models With Many Controls (2013, methodology): “In the context of instrumental regression, Belloni, Chernozhukov, and Hansen (2010) and Belloni et al. (2012) provided uniformly valid estimation and inference methods for instrumental variable models, using either post-selection or 1-regularization methods to estimate “optimal instruments.””
High-Dimensional Methods and Inference on Structural and Treatment Effects (2013, background): “…many-instrument asymptotics where the number of instruments p is allowed to increase with the sample size n in such a way that p < n and p/n → ρ < 1; see, for example, Bekker (1994), Chao and Swanson (2005), Hansen, Hausman, and Newey (2008), and Hausman, Newey, Woutersen, Chao, and Swanson (2012).”
High-dimensional econometrics and regularized GMM (2018, methodology): “This is particularly helpful when we allow for many approximate means as opposed to many exact means; see [7] for an application.”
On the multiply robust estimation of the mean of the g-functional (2017, background): “Belloni et al. (2010) proposed a cross-fitting estimator to relax the degree of sparsity required to obtain an asymptotically linear instrumental variable estimator.”
Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso (2014, methodology): “In the context of uniformly valid confidence bands for a single parameter the work of Belloni et al. (2014) is also relevant.”
Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso (2016, unknown): “For example, Caner (2009) and Fan and Liao (2014) consider covariate selection in GMM estimation; Belloni et al. (2012) and García (2011) consider selection of instruments in the GMM framework; Liao (2013) provides a shrinkage GMM method for moment selection and Cheng and Liao (2015) consider the…”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, background): “In previous work on sparsity (see, e.g., Belloni et al. (2012), Belloni et al. (2014c), Caner and Zhang (2014), Belloni et al. (2016), Kock (2016a), Kock (2016b), Callot et al. (2017), Caner and Kock (2018); see Belloni et al. (2014a) for a review), the sparsity assumption was used to select among…”
A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression (2014, methodology): “In other work on high-dimensional inference, the sparsity or similar conditions on the precision matrix Σ−1 are imposed, so as to obtain a consistent estimator for the asymptotic variance (see, for example, van de Geer et al., 2014; Belloni et al., 2012, 2013a).”
Asymptotics of Selective Inference (2015, methodology): “In the works of Belloni et al. (2012), Meinshausen et al. (2009), Zhang & Zhang (2014) and Javanmard & Montanari (2015), the authors proposed various ways of constructing confidence regions for the underlying parameters in the high-dimensional setting.”
The Unintended Impact of Pretrial Detention on Case Outcomes: Evidence from New York City Arraignments (2017, methodology): “We use a least absolute shrinkage and selection operator (Lasso) procedure described in Belloni et al. (2012) to reduce noise and avoid the many-instruments problem by selecting only the most informative of these instruments.”