Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain

By Alexandre Belloni, Daniel L. Chen, Victor Chernozhukov, Christian Hansen. 2012

Subfields: Statistics and Probability, Economics & Operations Research

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Citations in the corpus

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “Initial theoretical results were concerned obtaining valid inference on a coefficient in a high-dimensional regression, following model selection or regularization, with particular focus on the lasso (Belloni et al., 2012; Javanmard and Montanari, 2014; van de Geer et al., 2014).”
Counterfactual Prediction with Deep Instrumental Variables Networks (2016, background): “It is among the ﬁrst examples of such a strategy, building from an earlier literature that combines ML with econometrics, e.g., for sparse linear models in Belloni et al. [2012, 2014] and for trees and forests in Athey and Imbens [2016], Wager and Athey [2015].”
Generalized random forests (2016, methodology): “12In this simulation design, sparse series regression using the lasso, as in Belloni et al. (2012), might be expected to perform well.”
Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, methodology): “ for classication problems and as linear for regression problems.6 \Lasso" estimates an ‘ 1-penalized linear regression model using the data-driven penalty parameter selection rule developed in [4]. For \Lasso", we use a set of 275 potential control variables formed from the raw set of cov”
Sparse Signals in the Cross‐Section of Returns (2018, unknown): “Belloni et al. (2012, 2014) and Chernozhukov et al. (2015)”
Confidence intervals for causal effects with invalid instruments by using two‐stage hard thresholding with voting (2016, unknown): “(2014), Belloni et al. (2014) and Cai and Guo (2017) have provided confidence intervals for a treatment effect. In IV settings with high dimensional covariates (or IVs), Gautier and Tsybakov (2011), Belloni et al. (2012), Fan and Liao (2014) and Chernozhukov et al. (2015) have provided confidence in”
High-dimensional econometrics and regularized GMM (2018, methodology): “eloped in the context of providing uniformly valid inference about the parameters on endogenous variables in the IV context with many instruments to allow non-Gaussian heteroscedastic disturbances in [7]. [14], which to our knowledge provides the ﬁrst formal statement of the Neyman orthogonality con”
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments (2019, unknown): “…biases in estimating the nuisance functions by ML or nonparametric methods; for example, Belloni, Chernozhukov, and Hansen (2014), Farrell (2015), Belloni et al. (2017), Farrell, Liang, and Misra (2021b in Belloni et al. (2012) and the unconditional treatment effects in Belloni et al. (2017).”
Program evaluation and causal inference with high-dimensional data (2013, methodology): “Our procedures build upon Belloni et al. (2010) and Belloni et al. (2012), who were the first to demonstrate in a highly specialized context, that valid inference can proceed following model selection allowing for model selection mistakes under two conditions. We formulate and extend these two condi”
High-dimensional inference in misspecified linear models (2015, background): “ linear models (Meinshausen et al., 2009; Minnier et al., 2011; van de Geer et al., 2014), undirected graphical models (Ren et al., 2015; Jankova and van de Geer, 2014), instrumental variable models (Belloni et al., 2012) or very general models (Meinshausen and Buhlmann, 2010) have been considered a”
Post-Selection Inference for Generalized Linear Models With Many Controls (2013, unknown): “Condition WL(ii) has mild moment conditions that are used to apply self-normalized moderate deviation theory to control heteroscedastic non-Gaussian errors similar to [6] where there are no estimated weights.”
Dissecting the Effect of Credit Supply on Trade: Evidence from Matched Credit-Export Data (2011, methodology): “2, we use the Least Absolute Shrinkage and Selection Operator (LASSO) method in Belloni et al. (2012) to select the optimal parametrization of the instrument and to avoid choosing the functional form of the instrument in an ad hoc manner.”
Taming the Factor Zoo: A Test of New Factors (2019, unknown): “For the post-selection-inference method, see, for example, Belloni et al. (2012), Belloni et al. (2014b), and review articles by Belloni et al.”