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Abstract Views |
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12 months |
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Last month |
3 months |
12 months |
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| A $t$-test for synthetic controls |
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7 |
83 |
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5 |
32 |
264 |
| A Multi-Risk SIR Model with Optimally Targeted Lockdown |
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0 |
1 |
20 |
0 |
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8 |
70 |
| A Response to Philippe Lemoine's Critique on our Paper "Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S." |
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1 |
1 |
4 |
2 |
2 |
3 |
56 |
| A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees |
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2 |
43 |
1 |
2 |
3 |
59 |
| A lava attack on the recovery of sums of dense and sparse signals |
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3 |
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37 |
| A lava attack on the recovery of sums of dense and sparse signals |
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4 |
| A lava attack on the recovery of sums of dense and sparse signals |
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15 |
| A lava attack on the recovery of sums of dense and sparse signals |
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5 |
8 |
| A lava attack on the recovery of sums of dense and sparse signals |
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7 |
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2 |
6 |
47 |
| Adversarial Estimation of Riesz Representers |
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4 |
31 |
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5 |
13 |
62 |
| An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
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3 |
7 |
1 |
2 |
7 |
14 |
| An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
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1 |
62 |
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3 |
177 |
| An MCMC Approach to Classical Estimation |
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32 |
1 |
4 |
12 |
37 |
| An exact and robust conformal inference method for counterfactual and synthetic controls |
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1 |
1 |
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5 |
10 |
| An exact and robust conformal inference method for counterfactual and synthetic controls |
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1 |
7 |
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3 |
63 |
| Anti-concentration and honest, adaptive confidence bands |
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31 |
| Anti-concentration and honest, adaptive confidence bands |
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| Anti-concentration and honest, adaptive confidence bands |
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14 |
| Anti-concentration and honest, adaptive confidence bands |
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1 |
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1 |
3 |
| Applied Causal Inference Powered by ML and AI |
8 |
10 |
53 |
53 |
20 |
31 |
85 |
85 |
| Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models |
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0 |
21 |
22 |
0 |
0 |
22 |
23 |
| Arellano-bond lasso estimator for dynamic linear panel models |
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0 |
1 |
1 |
2 |
4 |
13 |
13 |
| Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals |
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0 |
0 |
17 |
1 |
3 |
10 |
32 |
| Automatic Debiased Machine Learning of Causal and Structural Effects |
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0 |
4 |
72 |
1 |
2 |
17 |
137 |
| Automatic Debiased Machine Learning via Riesz Regression |
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0 |
5 |
52 |
2 |
3 |
14 |
106 |
| Average and Quantile Effects in Nonseparable Panel Models |
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0 |
0 |
6 |
0 |
0 |
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34 |
| Best Linear Approximations to Set Identified Functions: With an Application to the Gender Wage Gap |
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32 |
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124 |
| Best linear approximations to set identified functions: with an application to the gender wage gap |
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1 |
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55 |
| Best linear approximations to set identified functions: with an application to the gender wage gap |
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1 |
1 |
3 |
4 |
| Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S |
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12 |
0 |
0 |
0 |
59 |
| Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S |
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0 |
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0 |
1 |
1 |
19 |
| Censored Quantile Instrumental Variable Estimation via Control Functions |
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0 |
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40 |
1 |
1 |
1 |
178 |
| Censored Quantile Instrumental Variable Estimation with Stata |
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0 |
7 |
1 |
1 |
2 |
62 |
| Censored Quantile Instrumental Variable Estimation with Stata |
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1 |
1 |
14 |
1 |
2 |
3 |
101 |
| Censored Quantile Instrumental Variable Estimation with Stata |
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0 |
0 |
11 |
0 |
0 |
1 |
58 |
| Central limit theorems and bootstrap in high dimensions |
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1 |
1 |
22 |
0 |
1 |
1 |
64 |
| Central limit theorems and bootstrap in high dimensions |
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0 |
1 |
1 |
0 |
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3 |
6 |
| Central limit theorems and bootstrap in high dimensions |
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0 |
0 |
0 |
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1 |
3 |
4 |
| Central limit theorems and bootstrap in high dimensions |
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0 |
0 |
4 |
0 |
0 |
0 |
41 |
| Central limit theorems and multiplier bootstrap when p is much larger than n |
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0 |
0 |
39 |
1 |
1 |
2 |
87 |
| Central limit theorems and multiplier bootstrap when p is much larger than n |
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2 |
4 |
1 |
2 |
7 |
12 |
| Closing the U.S. gender wage gap requires understanding its heterogeneity |
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0 |
2 |
68 |
0 |
1 |
6 |
135 |
| Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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0 |
0 |
0 |
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2 |
4 |
| Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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0 |
1 |
0 |
1 |
4 |
10 |
| Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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0 |
0 |
3 |
1 |
1 |
4 |
47 |
| Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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0 |
0 |
1 |
0 |
0 |
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25 |
| Conditional Quantile Processes based on Series or Many Regressors |
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1 |
7 |
0 |
1 |
4 |
84 |
| Conditional quantile processes based on series or many regressors |
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0 |
0 |
15 |
1 |
1 |
2 |
50 |
| Conditional quantile processes based on series or many regressors |
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0 |
1 |
49 |
0 |
0 |
2 |
113 |
| Conditional quantile processes based on series or many regressors |
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0 |
2 |
4 |
0 |
0 |
4 |
8 |
| Confidence bands for coefficients in high dimensional linear models with error-in-variables |
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0 |
28 |
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0 |
1 |
34 |
| Confidence bands for coefficients in high dimensional linear models with error-in-variables |
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0 |
0 |
0 |
0 |
1 |
3 |
7 |
| Constrained conditional moment restriction models |
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0 |
0 |
25 |
0 |
0 |
1 |
88 |
| Constrained conditional moment restriction models |
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0 |
0 |
1 |
0 |
0 |
0 |
2 |
| Correction to: Vector Quantile Regression and Optimal Transport, from Theory to Numerics |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Correction to: Vector Quantile Regression and Optimal Transport, from Theory to Numerics |
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0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Counterfactual analysis in R: a vignette |
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0 |
1 |
53 |
1 |
1 |
3 |
222 |
| Counterfactual analysis in R: a vignette |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
13 |
| Counterfactual: An R Package for Counterfactual Analysis |
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1 |
1 |
19 |
1 |
2 |
3 |
79 |
| De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers |
0 |
0 |
0 |
72 |
0 |
0 |
2 |
115 |
| Demand Analysis with Many Prices |
0 |
0 |
0 |
95 |
0 |
0 |
3 |
131 |
| Demand analysis with many prices |
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0 |
0 |
7 |
0 |
1 |
4 |
47 |
| Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK |
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2 |
4 |
60 |
0 |
3 |
13 |
129 |
| Distribution regression with sample selection and UK wage decomposition |
1 |
1 |
6 |
36 |
1 |
4 |
19 |
47 |
| Distribution regression with sample selection, with an application to wage decompositions in the UK |
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0 |
0 |
2 |
1 |
1 |
4 |
38 |
| Distributional conformal prediction |
0 |
0 |
1 |
2 |
1 |
1 |
3 |
8 |
| Distributional conformal prediction |
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0 |
1 |
43 |
1 |
1 |
6 |
137 |
| Double machine learning for treatment and causal parameters |
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2 |
5 |
117 |
1 |
8 |
24 |
526 |
| Double machine learning for treatment and causal parameters |
0 |
0 |
1 |
4 |
0 |
1 |
7 |
21 |
| Double/Debiased Machine Learning for Treatment and Causal Parameters |
23 |
76 |
358 |
1,049 |
65 |
213 |
881 |
2,693 |
| Double/Debiased Machine Learning for Treatment and Structural Parameters |
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2 |
7 |
118 |
12 |
38 |
93 |
405 |
| Double/de-biased machine learning using regularized Riesz representers |
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1 |
2 |
32 |
1 |
2 |
4 |
75 |
| Double/debiased machine learning for treatment and structural parameters |
0 |
0 |
1 |
34 |
4 |
8 |
21 |
108 |
| Double/debiased machine learning for treatment and structural parameters |
0 |
1 |
3 |
3 |
2 |
6 |
13 |
18 |
| DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python |
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0 |
3 |
18 |
1 |
3 |
12 |
70 |
| DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R |
0 |
1 |
4 |
60 |
0 |
2 |
8 |
104 |
| DoubleMLDeep: Estimation of Causal Effects with Multimodal Data |
0 |
1 |
15 |
19 |
0 |
5 |
29 |
36 |
| Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
24 |
| Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments |
0 |
0 |
25 |
25 |
0 |
0 |
14 |
14 |
| Estimation of treatment effects with high-dimensional controls |
0 |
0 |
0 |
38 |
0 |
1 |
2 |
74 |
| Estimation of treatment effects with high-dimensional controls |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Exact and robust conformal inference methods for predictive machine learning with dependent data |
0 |
0 |
0 |
69 |
1 |
2 |
4 |
70 |
| Extremal Quantile Regression: An Overview |
0 |
0 |
1 |
50 |
0 |
0 |
1 |
57 |
| Extremal quantile regression |
0 |
0 |
0 |
17 |
0 |
0 |
1 |
60 |
| Extremal quantile regression: an overview |
0 |
0 |
2 |
7 |
0 |
0 |
4 |
41 |
| Extremal quantile regression: an overview |
0 |
0 |
2 |
2 |
0 |
1 |
4 |
5 |
| Fast Algorithms for the Quantile Regression Process |
1 |
1 |
1 |
52 |
1 |
1 |
2 |
114 |
| Finite-Sample Inference Methods for Quantile Regression Models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
249 |
| Fischer-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
0 |
0 |
0 |
1 |
1 |
4 |
48 |
107 |
| Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
2 |
8 |
33 |
247 |
10 |
30 |
131 |
697 |
| Fisher-Schultz Lecture: Linear Estimation of Structural and Causal Effects for Nonseparable Panel Data |
3 |
14 |
14 |
14 |
5 |
16 |
16 |
16 |
| Fragility of Asymptotic Agreement under Bayesian Learning |
0 |
0 |
0 |
88 |
0 |
1 |
2 |
261 |
| Gaussian approximation of suprema of empirical processes |
0 |
0 |
1 |
6 |
1 |
1 |
4 |
48 |
| Gaussian approximation of suprema of empirical processes |
0 |
0 |
1 |
5 |
0 |
0 |
3 |
39 |
| Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
31 |
0 |
0 |
1 |
66 |
| Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
| Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
7 |
| Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
1 |
1 |
1 |
2 |
7 |
12 |
| Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
0 |
13 |
0 |
1 |
3 |
97 |
| Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
2 |
9 |
0 |
1 |
12 |
69 |
| Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
1 |
1 |
60 |
0 |
1 |
2 |
103 |
| Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
1 |
1 |
8 |
0 |
2 |
4 |
21 |
| Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
0 |
8 |
0 |
1 |
1 |
46 |
| Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
0 |
0 |
3 |
96 |
0 |
0 |
12 |
303 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
5 |
0 |
1 |
1 |
51 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
4 |
0 |
1 |
1 |
39 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
| Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
0 |
0 |
2 |
1 |
3 |
23 |
32 |
| Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
2 |
6 |
63 |
0 |
4 |
12 |
118 |
| Hedonic Prices and Quality Adjusted Price Indices Powered by AI |
0 |
0 |
1 |
16 |
1 |
2 |
6 |
14 |
| Hedonic prices and quality adjusted price indices powered by AI |
2 |
2 |
3 |
22 |
5 |
7 |
21 |
50 |
| High Dimensional Sparse Econometric Models: An Introduction |
0 |
0 |
0 |
13 |
0 |
1 |
6 |
53 |
| High dimensional methods and inference on structural and treatment effects |
0 |
0 |
0 |
22 |
1 |
2 |
2 |
111 |
| High dimensional methods and inference on structural and treatment effects |
0 |
0 |
1 |
1 |
0 |
2 |
5 |
9 |
| High-Dimensional Econometrics and Regularized GMM |
0 |
0 |
5 |
58 |
4 |
6 |
19 |
164 |
| High-Dimensional Metrics in R |
0 |
0 |
0 |
28 |
0 |
0 |
2 |
37 |
| High-dimensional Data Bootstrap |
0 |
1 |
1 |
37 |
0 |
1 |
2 |
23 |
| High-dimensional econometrics and regularized GMM |
0 |
0 |
0 |
13 |
0 |
1 |
10 |
83 |
| Honest confidence regions for a regression parameter in logistic regression with a large number of controls |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
6 |
| Honest confidence regions for a regression parameter in logistic regression with a large number of controls |
0 |
0 |
1 |
71 |
1 |
2 |
6 |
190 |
| Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study |
0 |
0 |
9 |
10 |
0 |
0 |
8 |
10 |
| IMPROVING ESTIMATES OF MONOTONE FUNCTIONS BY REARRANGEMENT |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
149 |
| INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS |
0 |
0 |
0 |
108 |
1 |
2 |
4 |
388 |
| Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game |
0 |
0 |
0 |
47 |
0 |
2 |
4 |
71 |
| Identification and Estimation of Marginal Effects in Nonlinear Panel Models |
0 |
0 |
0 |
47 |
0 |
2 |
5 |
175 |
| Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
1 |
106 |
0 |
0 |
2 |
323 |
| Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
1 |
31 |
0 |
0 |
3 |
117 |
| Identification of Hedonic Equilibrium and Nonseparable Simultaneous Equations |
0 |
0 |
0 |
24 |
0 |
0 |
0 |
7 |
| Identification of Hedonic Equilibrium and Nonseparable Simultaneous Equations |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
| Identification of hedonic equilibrium and nonseparable simultaneous equations |
0 |
0 |
0 |
20 |
0 |
0 |
1 |
53 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
64 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
5 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
0 |
0 |
0 |
113 |
| Improved Central Limit Theorem and bootstrap approximations in high dimensions |
0 |
0 |
0 |
26 |
0 |
1 |
8 |
93 |
| Improving Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
16 |
| Improving Point and Interval Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
4 |
1 |
1 |
1 |
19 |
| Improving estimates of monotone functions by rearrangement |
0 |
0 |
0 |
58 |
0 |
0 |
1 |
227 |
| Improving point and interval estimates of monotone functions by rearrangement |
0 |
0 |
0 |
65 |
0 |
0 |
0 |
314 |
| Improving point and interval estimators of monotone functions by rearrangement |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Improving point and interval estimators of monotone functions by rearrangement |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
46 |
| Inference for High-Dimensional Sparse Econometric Models |
0 |
0 |
2 |
12 |
1 |
3 |
11 |
77 |
| Inference for Low-Rank Models |
0 |
1 |
5 |
46 |
0 |
1 |
10 |
64 |
| Inference for best linear approximations to set identified functions |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
108 |
| Inference for best linear approximations to set identified functions |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Inference for extremal conditional quantile models, with an application to market and birthweight risks |
0 |
0 |
0 |
20 |
0 |
2 |
3 |
88 |
| Inference for heterogeneous effects using low-rank estimations |
0 |
0 |
1 |
17 |
1 |
3 |
8 |
50 |
| Inference for high-dimensional sparse econometric models |
0 |
0 |
0 |
56 |
0 |
1 |
4 |
187 |
| Inference in High Dimensional Panel Models with an Application to Gun Control |
0 |
0 |
0 |
7 |
1 |
2 |
6 |
46 |
| Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
| Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
25 |
0 |
0 |
2 |
86 |
| Inference on Counterfactual Distributions |
1 |
1 |
3 |
23 |
1 |
2 |
7 |
143 |
| Inference on Sets in Finance |
0 |
0 |
0 |
13 |
0 |
1 |
3 |
39 |
| Inference on Treatment Effects After Selection Amongst High-Dimensional Controls |
0 |
1 |
5 |
9 |
1 |
6 |
16 |
72 |
| Inference on average treatment effects in aggregate panel data settings |
0 |
1 |
2 |
40 |
0 |
1 |
4 |
162 |
| Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
0 |
14 |
2 |
2 |
2 |
25 |
| Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
0 |
14 |
0 |
0 |
2 |
44 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
3 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
1 |
0 |
2 |
2 |
3 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
434 |
0 |
0 |
1 |
937 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
113 |
0 |
1 |
4 |
351 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
893 |
0 |
0 |
3 |
1,919 |
| Inference on sets in finance |
0 |
0 |
0 |
70 |
0 |
0 |
3 |
168 |
| Inference on sets in finance |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Inference on sets in finance |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
51 |
| Inference on sets in finance |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
1 |
1 |
0 |
0 |
3 |
8 |
| Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
14 |
0 |
0 |
1 |
101 |
| Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
| Inference on treatment effects after selection amongst high-dimensional controls |
0 |
1 |
1 |
46 |
1 |
3 |
9 |
137 |
| Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework |
0 |
0 |
0 |
12 |
1 |
1 |
1 |
14 |
| Insights from optimal pandemic shielding in a multi-group SEIR framework |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
3 |
| Instrumental Variable Quantile Regression |
0 |
1 |
3 |
56 |
1 |
5 |
10 |
64 |
| Intersection Bounds: estimation and inference |
0 |
0 |
1 |
1 |
0 |
0 |
2 |
5 |
| Intersection Bounds: estimation and inference |
0 |
0 |
1 |
88 |
0 |
0 |
3 |
328 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
36 |
1 |
1 |
3 |
126 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
17 |
0 |
0 |
1 |
96 |
| L1-Penalized Quantile Regression in High-Dimensional Sparse Models |
0 |
0 |
2 |
34 |
0 |
0 |
4 |
121 |
| L1-Penalized quantile regression in high-dimensional sparse models |
0 |
0 |
0 |
73 |
0 |
0 |
1 |
270 |
| LASSO Methods for Gaussian Instrumental Variables Models |
0 |
0 |
0 |
10 |
1 |
1 |
1 |
48 |
| LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
23 |
| LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
4 |
0 |
1 |
2 |
20 |
| LASSO-Driven Inference in Time and Space |
0 |
0 |
1 |
37 |
0 |
1 |
4 |
97 |
| LASSO-Driven Inference in Time and Space |
2 |
2 |
2 |
39 |
2 |
2 |
4 |
83 |
| LASSO-driven inference in time and space |
0 |
0 |
0 |
5 |
0 |
1 |
1 |
34 |
| Learning and Disagreement in an Uncertain World |
0 |
0 |
1 |
120 |
2 |
2 |
9 |
520 |
| Learning and Disagreement in an Uncertain World |
0 |
0 |
0 |
102 |
0 |
1 |
5 |
386 |
| Local Identification of Nonparametric and Semiparametric Models |
0 |
1 |
1 |
13 |
0 |
2 |
4 |
136 |
| Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
49 |
1 |
1 |
2 |
173 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
31 |
1 |
2 |
13 |
136 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
80 |
| Locally Robust Semiparametric Estimation |
0 |
0 |
3 |
26 |
0 |
1 |
6 |
187 |
| Locally robust semiparametric estimation |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
| Locally robust semiparametric estimation |
0 |
0 |
0 |
32 |
1 |
3 |
4 |
167 |
| Locally robust semiparametric estimation |
0 |
1 |
1 |
18 |
0 |
3 |
6 |
93 |
| Long Story Short: Omitted Variable Bias in Causal Machine Learning |
0 |
2 |
10 |
187 |
1 |
5 |
29 |
124 |
| Long Story Short: Omitted Variable Bias in Causal Machine Learning |
0 |
0 |
3 |
32 |
1 |
3 |
14 |
157 |
| Mastering Panel 'Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
0 |
125 |
0 |
0 |
3 |
72 |
| Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
0 |
41 |
0 |
0 |
0 |
39 |
| Minimax Semiparametric Learning With Approximate Sparsity |
0 |
0 |
0 |
12 |
0 |
1 |
4 |
26 |
| Monge-Kantorovich Depth, Quantiles, Ranks and Signs |
0 |
0 |
0 |
40 |
0 |
0 |
0 |
105 |
| Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
52 |
| Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
41 |
| Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
0 |
3 |
0 |
1 |
1 |
7 |
| Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
2 |
| Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
4 |
| Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
4 |
| Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
3 |
| Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
76 |
| Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
0 |
9 |
0 |
0 |
0 |
52 |
| Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
1 |
5 |
0 |
0 |
1 |
12 |
| Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
1 |
50 |
0 |
1 |
4 |
98 |
| Network and panel quantile effects via distribution regression |
0 |
0 |
0 |
11 |
0 |
1 |
2 |
30 |
| Network and panel quantile effects via distribution regression |
0 |
0 |
1 |
2 |
1 |
1 |
3 |
23 |
| Nonparametric Identification in Panels using Quantiles |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
12 |
| Nonparametric Instrumental Variable Estimators of Structural Quantile Effects |
0 |
0 |
0 |
60 |
0 |
0 |
0 |
169 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
12 |
1 |
1 |
1 |
59 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
23 |
0 |
0 |
1 |
39 |
| Nonseparable Multinomial Choice Models in Cross-Section and Panel Data |
0 |
0 |
0 |
44 |
0 |
0 |
0 |
23 |
| Nonseparable multinomial choice models in cross-section and panel data |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
3 |
| Nonseparable multinomial choice models in cross-section and panel data |
0 |
0 |
0 |
15 |
0 |
0 |
0 |
24 |
| On the asymptotic theory for least squares series: pointwise and uniform results |
0 |
0 |
0 |
18 |
1 |
1 |
2 |
67 |
| On the asymptotic theory for least squares series: pointwise and uniform results |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| On the computational complexity of MCMC-based estimators in large samples |
0 |
0 |
0 |
20 |
2 |
2 |
4 |
80 |
| Optimal Targeted Lockdowns in a Multi-Group SIR Model |
0 |
0 |
3 |
113 |
0 |
5 |
24 |
645 |
| Parameter Set Inference in a Class of Econometric Models |
0 |
0 |
0 |
1 |
0 |
1 |
7 |
677 |
| Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables |
0 |
0 |
0 |
5 |
0 |
1 |
4 |
52 |
| Pivotal estimation via square-root lasso in nonparametric regression |
0 |
0 |
0 |
17 |
1 |
1 |
2 |
78 |
| Pivotal estimation via square-root lasso in nonparametric regression |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
| Plug-in regularized estimation of high dimensional parameters in nonlinear semiparametric models |
0 |
0 |
0 |
38 |
0 |
1 |
3 |
111 |
| Post-Selection Inference for Generalized Linear Models with Many Controls |
0 |
0 |
3 |
17 |
0 |
1 |
4 |
47 |
| Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments |
0 |
1 |
1 |
4 |
0 |
2 |
4 |
35 |
| Post-l1-penalized estimators in high-dimensional linear regression models |
0 |
0 |
0 |
50 |
0 |
0 |
1 |
165 |
| Post-selection and post-regularization inference in linear models with many controls and instruments |
0 |
0 |
0 |
40 |
0 |
1 |
9 |
151 |
| Post-selection and post-regularization inference in linear models with many controls and instruments |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
5 |
| Posterior Inference in Curved Exponential Families under Increasing Dimensions |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
5 |
| Posterior inference in curved exponential families under increasing dimensions |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
27 |
| Posterior inference in curved exponential families under increasing dimensions |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
2 |
| Program Evaluation and Causal Inference with High-Dimensional Data |
0 |
0 |
0 |
12 |
0 |
2 |
7 |
72 |
| Program evaluation and causal inference with high-dimensional data |
0 |
0 |
0 |
1 |
1 |
2 |
5 |
11 |
| Program evaluation and causal inference with high-dimensional data |
0 |
0 |
0 |
27 |
0 |
0 |
1 |
120 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
6 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
120 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
78 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
9 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
5 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
75 |
0 |
0 |
0 |
200 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
11 |
0 |
1 |
2 |
89 |
| QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING |
0 |
0 |
0 |
71 |
1 |
1 |
2 |
339 |
| Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
0 |
0 |
3 |
1 |
1 |
2 |
44 |
| Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
0 |
2 |
49 |
1 |
1 |
6 |
96 |
| Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk |
0 |
0 |
0 |
20 |
0 |
2 |
3 |
49 |
| Quantile Models with Endogeneity |
0 |
0 |
1 |
4 |
0 |
0 |
2 |
56 |
| Quantile Regression under Misspecification |
0 |
0 |
0 |
2 |
0 |
1 |
6 |
452 |
| Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
0 |
0 |
3 |
287 |
1 |
1 |
10 |
945 |
| Quantile Regression with Censoring and Endogeneity |
0 |
0 |
3 |
58 |
0 |
0 |
4 |
188 |
| Quantile Regression with Censoring and Endogeneity |
0 |
1 |
1 |
112 |
0 |
2 |
2 |
364 |
| Quantile Regression with Censoring and Endogeneity |
0 |
1 |
1 |
5 |
0 |
1 |
1 |
108 |
| Quantile and Average Effects in Nonseparable Panel Models |
0 |
0 |
0 |
25 |
0 |
0 |
0 |
99 |
| Quantile and Probability Curves Without Crossing |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
29 |
| Quantile and Probability Curves without Crossing |
0 |
0 |
0 |
18 |
0 |
3 |
5 |
141 |
| Quantile and Probability Curves without Crossing |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
47 |
| Quantile and average effects in nonseparable panel models |
0 |
0 |
0 |
43 |
0 |
0 |
1 |
113 |
| Quantile and probability curves without crossing |
0 |
0 |
0 |
68 |
0 |
1 |
2 |
272 |
| Quantile graphical models: prediction and conditional independence with applications to systemic risk |
0 |
0 |
0 |
34 |
2 |
2 |
2 |
37 |
| Quantile graphical models: prediction and conditional independence with applications to systemic risk |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
13 |
| Quantile models with endogeneity |
0 |
0 |
1 |
90 |
0 |
1 |
2 |
242 |
| Quantile models with endogeneity |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Quantile regression with censoring and endogeneity |
0 |
0 |
0 |
40 |
0 |
0 |
3 |
138 |
| Quantreg.nonpar: an R package for performing nonparametric series quantile regression |
0 |
0 |
0 |
19 |
0 |
0 |
2 |
130 |
| Quantreg.nonpar: an R package for performing nonparametric series quantile regression |
0 |
0 |
2 |
3 |
1 |
2 |
10 |
19 |
| Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
15 |
| Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
| Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Rearranging Edgeworth-Cornish-Fisher expansions |
0 |
0 |
0 |
90 |
0 |
0 |
0 |
331 |
| Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models |
0 |
0 |
2 |
37 |
0 |
1 |
3 |
60 |
| RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests |
0 |
0 |
1 |
34 |
1 |
2 |
4 |
44 |
| Robust inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
19 |
0 |
0 |
0 |
102 |
| Robust inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Semi-Parametric Efficient Policy Learning with Continuous Actions |
0 |
0 |
0 |
7 |
1 |
2 |
2 |
15 |
| Semi-Parametric Efficient Policy Learning with Continuous Actions |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
24 |
| Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
90 |
| Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models |
0 |
0 |
0 |
28 |
0 |
0 |
0 |
59 |
| Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
| Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
35 |
| Set identification with Tobin regressors |
0 |
0 |
1 |
64 |
0 |
0 |
1 |
178 |
| Shape-Enforcing Operators for Point and Interval Estimators |
0 |
0 |
0 |
30 |
0 |
1 |
3 |
70 |
| Simultaneous Confidence Intervals for High-dimensional Linear Models with Many Endogenous Variables |
0 |
0 |
0 |
30 |
0 |
0 |
0 |
23 |
| Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables |
0 |
0 |
0 |
4 |
2 |
2 |
3 |
25 |
| Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions |
0 |
0 |
0 |
93 |
2 |
5 |
8 |
214 |
| Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
24 |
| Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models |
0 |
0 |
0 |
15 |
0 |
2 |
3 |
42 |
| Single market non-parametric identification of multi-attribute hedonic equilibrium models |
0 |
0 |
0 |
4 |
0 |
1 |
1 |
17 |
| Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results |
0 |
0 |
1 |
9 |
0 |
0 |
3 |
44 |
| SortedEffects: Sorted Causal Effects in R |
0 |
0 |
0 |
5 |
0 |
1 |
1 |
29 |
| Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain |
0 |
0 |
1 |
18 |
0 |
0 |
8 |
77 |
| Sparse models and methods for optimal instruments with an application to eminent domain |
0 |
0 |
0 |
43 |
0 |
0 |
4 |
158 |
| Subvector Inference in Partially Identified Models with Many Moment Inequalities |
0 |
0 |
0 |
20 |
0 |
1 |
1 |
26 |
| Subvector inference in PI models with many moment inequalities |
0 |
0 |
0 |
20 |
1 |
2 |
4 |
16 |
| Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls" |
0 |
0 |
1 |
2 |
0 |
0 |
4 |
22 |
| Testing Many Moment Inequalities |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Testing Many Moment Inequalities |
1 |
1 |
1 |
13 |
1 |
1 |
2 |
79 |
| Testing many moment inequalities |
0 |
0 |
0 |
15 |
0 |
0 |
0 |
38 |
| Testing many moment inequalities |
0 |
0 |
1 |
35 |
0 |
0 |
2 |
89 |
| Testing many moment inequalities |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Testing many moment inequalities |
1 |
1 |
1 |
2 |
1 |
1 |
2 |
3 |
| The Association of Opening K-12 Schools and Colleges with the Spread of Covid-19 in the United States: County-Level Panel Data Analysis |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
16 |
| The Association of Opening K-12 Schools with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
27 |
| The Impact of Big Data on Firm Performance: An Empirical Investigation |
0 |
1 |
19 |
188 |
0 |
8 |
47 |
384 |
| The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
0 |
1 |
15 |
1 |
1 |
9 |
63 |
| The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
14 |
0 |
2 |
2 |
79 |
| The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
7 |
| Toward personalized inference on individual treatment effects |
0 |
0 |
3 |
3 |
0 |
0 |
3 |
3 |
| Uniform Inference in High-Dimensional Gaussian Graphical Models |
0 |
0 |
0 |
31 |
0 |
0 |
0 |
39 |
| Uniform Inference on High-dimensional Spatial Panel Networks |
0 |
0 |
0 |
12 |
1 |
1 |
3 |
54 |
| Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
27 |
| Uniform inference in high-dimensional Gaussian graphical models |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
15 |
| Uniform post selection inference for LAD regression and other Z-estimation problems |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
44 |
| Uniform post selection inference for LAD regression and other Z-estimation problems |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
| Uniform post selection inference for LAD regression and other z-estimation problems |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
74 |
| Uniform post selection inference for LAD regression and other z-estimation problems |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Uniform post selection inference for LAD regression models |
0 |
0 |
0 |
31 |
0 |
0 |
1 |
96 |
| Uniform post selection inference for LAD regression models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models |
0 |
1 |
3 |
13 |
0 |
2 |
6 |
61 |
| Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach |
0 |
0 |
1 |
4 |
0 |
0 |
2 |
25 |
| Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R) |
0 |
0 |
1 |
22 |
0 |
1 |
2 |
48 |
| Valid post-selection and post-regularization inference: An elementary, general approach |
0 |
0 |
0 |
0 |
2 |
2 |
4 |
5 |
| Valid post-selection and post-regularization inference: An elementary, general approach |
0 |
0 |
0 |
22 |
0 |
0 |
1 |
44 |
| Valid post-selection inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
3 |
| Valid post-selection inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
17 |
0 |
1 |
2 |
57 |
| Valid simultaneous inference in high-dimensional settings (with the HDM package for R) |
0 |
0 |
0 |
10 |
0 |
1 |
1 |
36 |
| Vector Quantile Regression |
0 |
0 |
0 |
3 |
2 |
3 |
5 |
51 |
| Vector Quantile Regression: An Optimal Transport Approach |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Vector Quantile Regression: An Optimal Transport Approach |
0 |
0 |
1 |
25 |
0 |
0 |
2 |
85 |
| Vector quantile regression |
0 |
0 |
0 |
9 |
0 |
0 |
0 |
44 |
| Vector quantile regression |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
3 |
| Vector quantile regression and optimal transport, from theory to numerics |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
16 |
| Vector quantile regression: an optimal transport approach |
0 |
0 |
0 |
21 |
0 |
0 |
0 |
52 |
| Vector quantile regression: an optimal transport approach |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Welfare Analysis in Dynamic Models |
0 |
0 |
0 |
17 |
1 |
4 |
6 |
37 |
| hdm: High-Dimensional Metrics |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
35 |
| hdm: High-Dimensional Metrics |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
29 |
| hdm: High-Dimensional Metrics |
1 |
1 |
2 |
2 |
2 |
2 |
6 |
9 |
| quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression |
0 |
0 |
0 |
6 |
1 |
4 |
4 |
53 |
| Total Working Papers |
47 |
153 |
760 |
10,614 |
235 |
683 |
2,563 |
33,444 |
| Journal Article |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A simple and general debiased machine learning theorem with finite-sample guarantees |
0 |
0 |
0 |
0 |
1 |
1 |
5 |
6 |
| ADMISSIBLE INVARIANT SIMILAR TESTS FOR INSTRUMENTAL VARIABLES REGRESSION |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
56 |
| An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
0 |
2 |
10 |
27 |
1 |
6 |
30 |
70 |
| An IV Model of Quantile Treatment Effects |
0 |
1 |
6 |
453 |
0 |
3 |
13 |
1,311 |
| An MCMC approach to classical estimation |
1 |
3 |
13 |
538 |
2 |
7 |
29 |
1,145 |
| Automatic Debiased Machine Learning of Causal and Structural Effects |
0 |
1 |
9 |
37 |
0 |
5 |
28 |
113 |
| Average and Quantile Effects in Nonseparable Panel Models |
0 |
0 |
0 |
38 |
0 |
3 |
6 |
193 |
| Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S |
1 |
1 |
3 |
25 |
2 |
3 |
12 |
116 |
| Censored quantile instrumental-variable estimation with Stata |
0 |
1 |
2 |
12 |
0 |
1 |
4 |
60 |
| Comment |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
| Conditional quantile processes based on series or many regressors |
0 |
0 |
4 |
38 |
0 |
1 |
11 |
106 |
| Conditional value-at-risk: Aspects of modeling and estimation |
0 |
0 |
1 |
880 |
0 |
0 |
3 |
2,137 |
| Constrained Conditional Moment Restriction Models |
0 |
1 |
1 |
4 |
1 |
2 |
10 |
26 |
| Correction to: Vector quantile regression and optimal transport, from theory to numerics |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
5 |
| Debiased machine learning of conditional average treatment effects and other causal functions |
0 |
0 |
2 |
11 |
0 |
1 |
4 |
20 |
| Debiased machine learning of global and local parameters using regularized Riesz representers |
0 |
0 |
0 |
3 |
0 |
0 |
2 |
8 |
| Double/Debiased/Neyman Machine Learning of Treatment Effects |
0 |
0 |
2 |
72 |
1 |
7 |
15 |
281 |
| Double/debiased machine learning for treatment and structural parameters |
3 |
6 |
21 |
102 |
10 |
25 |
99 |
389 |
| Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
0 |
0 |
0 |
2 |
0 |
0 |
3 |
29 |
| Estimation and Confidence Regions for Parameter Sets in Econometric Models |
2 |
2 |
4 |
314 |
2 |
5 |
12 |
711 |
| Fast algorithms for the quantile regression process |
0 |
1 |
1 |
5 |
1 |
5 |
8 |
25 |
| Finite sample inference for quantile regression models |
0 |
0 |
2 |
65 |
0 |
1 |
7 |
286 |
| Fragility of asymptotic agreement under Bayesian learning |
0 |
0 |
0 |
47 |
2 |
4 |
6 |
229 |
| Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
1 |
1 |
1 |
10 |
1 |
3 |
6 |
47 |
| High-Dimensional Methods and Inference on Structural and Treatment Effects |
1 |
1 |
6 |
51 |
4 |
7 |
28 |
274 |
| Identification of Hedonic Equilibrium and Nonseparable Simultaneous Equations |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
59 |
| Implementing intersection bounds in Stata |
0 |
3 |
4 |
42 |
1 |
4 |
7 |
161 |
| Improving point and interval estimators of monotone functions by rearrangement |
0 |
0 |
0 |
34 |
0 |
3 |
4 |
138 |
| Inference approaches for instrumental variable quantile regression |
0 |
0 |
2 |
462 |
1 |
1 |
14 |
1,176 |
| Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
0 |
1 |
1 |
36 |
0 |
4 |
10 |
158 |
| Inference in High-Dimensional Panel Models With an Application to Gun Control |
0 |
2 |
6 |
60 |
2 |
7 |
29 |
214 |
| Inference on Causal and Structural Parameters using Many Moment Inequalities |
0 |
0 |
1 |
10 |
0 |
2 |
5 |
76 |
| Inference on Counterfactual Distributions |
1 |
1 |
2 |
367 |
1 |
3 |
15 |
965 |
| Inference on Treatment Effects after Selection among High-Dimensional Controls†|
1 |
3 |
7 |
89 |
3 |
9 |
25 |
315 |
| Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence |
0 |
0 |
1 |
2 |
0 |
0 |
3 |
6 |
| Inference on sets in finance |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
34 |
| Instrumental quantile regression inference for structural and treatment effect models |
1 |
1 |
12 |
522 |
2 |
7 |
34 |
1,093 |
| Instrumental variable estimation of nonseparable models |
0 |
0 |
0 |
173 |
0 |
0 |
1 |
355 |
| Instrumental variable quantile regression: A robust inference approach |
3 |
5 |
31 |
457 |
6 |
10 |
55 |
962 |
| Intersection Bounds: Estimation and Inference |
0 |
0 |
0 |
20 |
0 |
1 |
3 |
192 |
| Introduction |
0 |
0 |
0 |
33 |
0 |
0 |
1 |
136 |
| Likelihood Estimation and Inference in a Class of Nonregular Econometric Models |
0 |
0 |
0 |
86 |
0 |
0 |
1 |
294 |
| Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
18 |
0 |
0 |
1 |
111 |
| Locally Robust Semiparametric Estimation |
0 |
1 |
4 |
17 |
2 |
3 |
16 |
50 |
| Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
1 |
28 |
1 |
1 |
7 |
86 |
| Network and panel quantile effects via distribution regression |
1 |
2 |
4 |
4 |
1 |
4 |
9 |
9 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
1 |
16 |
0 |
0 |
1 |
93 |
| Nonseparable multinomial choice models in cross-section and panel data |
1 |
1 |
2 |
13 |
3 |
3 |
5 |
65 |
| Optimal Targeted Lockdowns in a Multigroup SIR Model |
2 |
3 |
5 |
35 |
3 |
8 |
28 |
187 |
| Post-Selection Inference for Generalized Linear Models With Many Controls |
0 |
0 |
2 |
53 |
0 |
0 |
11 |
166 |
| Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments |
0 |
0 |
2 |
38 |
1 |
3 |
11 |
228 |
| Posterior inference in curved exponential families under increasing dimensions |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
30 |
| Program Evaluation and Causal Inference With High‐Dimensional Data |
1 |
1 |
2 |
33 |
3 |
3 |
8 |
139 |
| Quantile Models with Endogeneity |
0 |
0 |
1 |
49 |
1 |
3 |
6 |
209 |
| Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
1 |
1 |
3 |
400 |
2 |
3 |
8 |
1,239 |
| Quantile and Probability Curves Without Crossing |
0 |
0 |
0 |
82 |
0 |
2 |
3 |
307 |
| Quantile regression with censoring and endogeneity |
1 |
2 |
6 |
89 |
1 |
6 |
15 |
397 |
| Rearranging Edgeworth–Cornish–Fisher expansions |
0 |
0 |
0 |
31 |
0 |
0 |
1 |
133 |
| Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
1 |
1 |
2 |
2 |
26 |
| Set identification and sensitivity analysis with Tobin regressors |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
73 |
| Some new asymptotic theory for least squares series: Pointwise and uniform results |
0 |
1 |
7 |
89 |
3 |
5 |
29 |
314 |
| Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain |
0 |
0 |
4 |
113 |
0 |
2 |
20 |
538 |
| Square-root lasso: pivotal recovery of sparse signals via conic programming |
0 |
0 |
1 |
22 |
0 |
1 |
4 |
130 |
| The Effects of 401(K) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis |
0 |
4 |
22 |
235 |
2 |
12 |
51 |
600 |
| The Impact of Big Data on Firm Performance: An Empirical Investigation |
0 |
1 |
7 |
90 |
1 |
3 |
20 |
380 |
| The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
1 |
1 |
17 |
1 |
3 |
5 |
92 |
| The association of opening K–12 schools with the spread of COVID-19 in the United States: County-level panel data analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
| The reduced form: A simple approach to inference with weak instruments |
0 |
0 |
8 |
235 |
1 |
1 |
15 |
549 |
| Three-Step Censored Quantile Regression and Extramarital Affairs |
0 |
0 |
0 |
66 |
1 |
1 |
2 |
261 |
| Uniform inference in high-dimensional Gaussian graphical models |
0 |
0 |
1 |
2 |
0 |
0 |
5 |
7 |
| Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems |
0 |
0 |
0 |
8 |
0 |
0 |
4 |
44 |
| Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models |
0 |
0 |
1 |
4 |
1 |
1 |
2 |
30 |
| Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach |
0 |
1 |
5 |
26 |
0 |
2 |
14 |
117 |
| Vector quantile regression and optimal transport, from theory to numerics |
0 |
0 |
0 |
2 |
0 |
0 |
2 |
13 |
| Vector quantile regression beyond the specified case |
0 |
0 |
0 |
8 |
1 |
1 |
2 |
29 |
| Total Journal Articles |
22 |
56 |
245 |
6,971 |
74 |
214 |
860 |
20,639 |
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