Advances in Economics and Econometrics: Inference for High-Dimensional Sparse Econometric Models

By Alexandre Belloni, Victor Chernozhukov, Christian Hansen. 2013

135 citations recorded by the source corpus.

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, positive): “Our paper also builds upon and contributes to the literature on modern orthogonal/debiased estimation and inference [Zhang and Zhang (2014); Belloni et al. (2011, 2014, 2015); Javanmard and Montanari (2014a,b, 2018); Van de Geer et al. (2014); Ning and Liu (2017); Chernozhukov et al. (2015); Neykov…”
Double machine learning for treatment and causal parameters (2016, methodology): “This approach represents a generalization of the approach of [7, 8] considered for the case of Lasso without cross-fitting.”
Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso (2014, methodology): “Statistical inference in the sense of constructing tests and confidence bands was to the best of our knowledge considered for the first time in a seminal series of papers by Belloni et al. (2010, 2012, 2011b, 2014, 2011a).”
Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems (2013, unknown): “The latter feature is critical in achieving uniformity over a la rge class of data generating processes, similarly to the results for instrumental regression and me an regression studied in [34] and [2, 4, 5].”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “Our treatment builds upon the sample-splitting ideas employed in Belloni et al. (2010) and Belloni et al. (2012) who considered sample-splitting in a high-dimensional sparse optimal IV model to weaken the sparsity condition mentioned in the previous paragraph to s n.”
Program evaluation and causal inference with high-dimensional data (2013, methodology): “Specifically, we apply the Square-Root Lasso of Belloni et al. (2011) with outcome Y and covariates (D,D ∗ X1, ..., D ∗Xp, (1−D), (1−D) ∗X1, ..., (1−D) ∗Xp) to select variables.”
Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models (2013, methodology): “In the case of linear mean regression, [5] proposes a double selection inference in a parametric setting with homoscedastic Gaussian errors; [6] studies a double selection procedure in a non-parametric setting with heteroscedastic errors; [40] and [37] propose methods based on l1-penalized estimatio”
Program evaluation with high-dimensional data (2013, unknown): “To the best of our knowledge, Belloni et al. (2010) and Belloni et al. (2012) were the first to use the orthogonality (1.2) to expressly address the question of the uniform post-selection inference without imposing the “beta-min” conditions, either in high-dimensional settings with p n or in…”
Valid confidence intervals for post-model-selection predictors (2014, methodology): “o, the work of Lockhart et al. (2014), Wasserman and Roeder (2009), and Wasserman (2014) should be mentioned here. For the LASSO, in particular, a de-sparsifying method has recently been developed by Belloni et al. (2011, 2014), van de Geer et al. (2014), and Zhang and Zhang (2014). Another strand o”
High-dimensional econometrics and regularized GMM (2018, background): “See also [13], [65], [82], [16], and [12], among others,”
Pivotal estimation via square-root Lasso in nonparametric regression (2011, methodology): “In the linear regression model, this estimator is first-order equivalent to, but different in finite samples from, a one-step correction from the scaled Lasso proposed in [55]; in the partially linear model, it is equivalent to the postdouble selection method of [9, 10].”
Communication-efficient Sparse Regression (2017, methodology): “The parameter λ can be chosen independently of σ, if we replace the lasso with the sqrt-lasso Belloni et al. (2011); all of the same theoretical guarantees still apply, since the sqrt-lasso has the same consistency guarantees. For generalized linear models, the oracle choice of λ depends on known qu”
Bootstrapping and sample splitting for high-dimensional, assumption-lean inference (2016, background): “While this is a larger literature than can be addressed completely here, it includes early work on model selection [35] and model averaging interpretations [31]; the impossibility results of [38] and [16] on random X and model misspecification; methods based on resampling or sample splitting [18, 19”