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A $t$-test for synthetic controls |
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84 |
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A Multi-Risk SIR Model with Optimally Targeted Lockdown |
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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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A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees |
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A lava attack on the recovery of sums of dense and sparse signals |
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A lava attack on the recovery of sums of dense and sparse signals |
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A lava attack on the recovery of sums of dense and sparse signals |
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A lava attack on the recovery of sums of dense and sparse signals |
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A lava attack on the recovery of sums of dense and sparse signals |
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47 |
Adventures in Demand Analysis Using AI |
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Adversarial Estimation of Riesz Representers |
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An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
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177 |
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
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An MCMC Approach to Classical Estimation |
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An exact and robust conformal inference method for counterfactual and synthetic controls |
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An exact and robust conformal inference method for counterfactual and synthetic controls |
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Anti-concentration and honest, adaptive confidence bands |
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Anti-concentration and honest, adaptive confidence bands |
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Anti-concentration and honest, adaptive confidence bands |
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Anti-concentration and honest, adaptive confidence bands |
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Applied Causal Inference Powered by ML and AI |
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Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models |
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Arellano-bond lasso estimator for dynamic linear panel models |
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Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals |
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Automatic Debiased Machine Learning of Causal and Structural Effects |
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Automatic Debiased Machine Learning via Riesz Regression |
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Automatic Doubly Robust Forests |
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Average and Quantile Effects in Nonseparable Panel Models |
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Best Linear Approximations to Set Identified Functions: With an Application to the Gender Wage Gap |
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Best linear approximations to set identified functions: with an application to the gender wage gap |
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Best linear approximations to set identified functions: with an application to the gender wage gap |
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55 |
Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S |
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12 |
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59 |
Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S |
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20 |
Censored Quantile Instrumental Variable Estimation via Control Functions |
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40 |
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178 |
Censored Quantile Instrumental Variable Estimation with Stata |
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14 |
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Censored Quantile Instrumental Variable Estimation with Stata |
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11 |
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Censored Quantile Instrumental Variable Estimation with Stata |
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Central limit theorems and bootstrap in high dimensions |
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Central limit theorems and bootstrap in high dimensions |
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Central limit theorems and bootstrap in high dimensions |
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22 |
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64 |
Central limit theorems and bootstrap in high dimensions |
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41 |
Central limit theorems and multiplier bootstrap when p is much larger than n |
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Central limit theorems and multiplier bootstrap when p is much larger than n |
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39 |
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87 |
Closing the U.S. gender wage gap requires understanding its heterogeneity |
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68 |
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Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments" |
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Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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47 |
Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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10 |
Conditional Influence Functions |
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Conditional Quantile Processes based on Series or Many Regressors |
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Conditional Rank-Rank Regression |
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Conditional Rank-Rank Regression |
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13 |
13 |
Conditional quantile processes based on series or many regressors |
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49 |
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2 |
113 |
Conditional quantile processes based on series or many regressors |
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4 |
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8 |
Conditional quantile processes based on series or many regressors |
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15 |
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50 |
Confidence bands for coefficients in high dimensional linear models with error-in-variables |
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28 |
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1 |
34 |
Confidence bands for coefficients in high dimensional linear models with error-in-variables |
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7 |
Constrained conditional moment restriction models |
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3 |
Constrained conditional moment restriction models |
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25 |
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89 |
Correction to: Vector Quantile Regression and Optimal Transport, from Theory to Numerics |
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1 |
Correction to: Vector Quantile Regression and Optimal Transport, from Theory to Numerics |
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1 |
Counterfactual analysis in R: a vignette |
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13 |
Counterfactual analysis in R: a vignette |
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53 |
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2 |
222 |
Counterfactual: An R Package for Counterfactual Analysis |
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1 |
1 |
19 |
0 |
2 |
3 |
79 |
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers |
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72 |
2 |
2 |
3 |
117 |
Demand Analysis with Many Prices |
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95 |
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3 |
131 |
Demand analysis with many prices |
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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 |
2 |
5 |
15 |
131 |
Distribution regression with sample selection and UK wage decomposition |
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5 |
36 |
1 |
5 |
18 |
48 |
Distribution regression with sample selection, with an application to wage decompositions in the UK |
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1 |
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4 |
39 |
Distributional conformal prediction |
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1 |
2 |
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8 |
Distributional conformal prediction |
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43 |
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6 |
137 |
Double machine learning for treatment and causal parameters |
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4 |
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7 |
22 |
Double machine learning for treatment and causal parameters |
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117 |
4 |
8 |
28 |
530 |
Double/Debiased Machine Learning for Treatment and Causal Parameters |
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61 |
320 |
1,053 |
20 |
169 |
812 |
2,713 |
Double/Debiased Machine Learning for Treatment and Structural Parameters |
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7 |
118 |
5 |
26 |
92 |
410 |
Double/de-biased machine learning using regularized Riesz representers |
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0 |
2 |
32 |
1 |
2 |
5 |
76 |
Double/debiased machine learning for treatment and structural parameters |
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0 |
1 |
34 |
2 |
7 |
22 |
110 |
Double/debiased machine learning for treatment and structural parameters |
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0 |
3 |
3 |
0 |
3 |
13 |
18 |
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python |
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0 |
2 |
18 |
1 |
4 |
10 |
71 |
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R |
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3 |
60 |
0 |
1 |
7 |
104 |
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data |
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19 |
1 |
4 |
23 |
37 |
Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
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1 |
1 |
1 |
1 |
1 |
2 |
2 |
Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
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0 |
0 |
7 |
0 |
0 |
1 |
24 |
Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments |
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1 |
24 |
26 |
2 |
2 |
13 |
16 |
Estimation of treatment effects with high-dimensional controls |
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0 |
0 |
0 |
0 |
0 |
1 |
1 |
Estimation of treatment effects with high-dimensional controls |
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0 |
0 |
38 |
0 |
1 |
1 |
74 |
Exact and robust conformal inference methods for predictive machine learning with dependent data |
1 |
1 |
1 |
70 |
1 |
2 |
4 |
71 |
Extremal Quantile Regression: An Overview |
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0 |
1 |
50 |
1 |
1 |
2 |
58 |
Extremal quantile regression |
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0 |
0 |
17 |
1 |
1 |
2 |
61 |
Extremal quantile regression: an overview |
0 |
0 |
2 |
2 |
0 |
0 |
4 |
5 |
Extremal quantile regression: an overview |
0 |
0 |
2 |
7 |
0 |
0 |
4 |
41 |
Fast Algorithms for the Quantile Regression Process |
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1 |
1 |
52 |
0 |
1 |
2 |
114 |
Finite-Sample Inference Methods for Quantile Regression Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
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 |
39 |
108 |
Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
3 |
9 |
30 |
250 |
11 |
33 |
124 |
708 |
Fisher-Schultz Lecture: Linear Estimation of Structural and Causal Effects for Nonseparable Panel Data |
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15 |
15 |
15 |
2 |
18 |
18 |
18 |
Fragility of Asymptotic Agreement under Bayesian Learning |
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88 |
1 |
2 |
3 |
262 |
Gaussian approximation of suprema of empirical processes |
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0 |
0 |
0 |
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0 |
1 |
3 |
Gaussian approximation of suprema of empirical processes |
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0 |
0 |
0 |
0 |
2 |
5 |
7 |
Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
Gaussian approximation of suprema of empirical processes |
0 |
0 |
1 |
6 |
0 |
1 |
3 |
48 |
Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
31 |
0 |
0 |
1 |
66 |
Gaussian approximation of suprema of empirical processes |
0 |
0 |
1 |
5 |
0 |
0 |
3 |
39 |
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
1 |
1 |
0 |
2 |
7 |
12 |
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
0 |
13 |
0 |
0 |
3 |
97 |
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
1 |
9 |
2 |
3 |
13 |
71 |
Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
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0 |
1 |
60 |
1 |
1 |
3 |
104 |
Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
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0 |
1 |
8 |
0 |
1 |
3 |
21 |
Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
46 |
Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
0 |
0 |
3 |
96 |
1 |
1 |
11 |
304 |
Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
51 |
Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
39 |
Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
0 |
0 |
2 |
0 |
3 |
20 |
32 |
Generic machine learning inference on heterogenous treatment effects in randomized experiments |
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1 |
6 |
63 |
1 |
3 |
12 |
119 |
Hedonic Prices and Quality Adjusted Price Indices Powered by AI |
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0 |
1 |
16 |
0 |
2 |
6 |
14 |
Hedonic prices and quality adjusted price indices powered by AI |
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2 |
3 |
22 |
1 |
7 |
21 |
51 |
High Dimensional Sparse Econometric Models: An Introduction |
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13 |
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1 |
5 |
53 |
High dimensional methods and inference on structural and treatment effects |
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0 |
1 |
1 |
3 |
5 |
10 |
High dimensional methods and inference on structural and treatment effects |
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0 |
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22 |
1 |
2 |
3 |
112 |
High-Dimensional Econometrics and Regularized GMM |
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3 |
58 |
3 |
8 |
18 |
167 |
High-Dimensional Metrics in R |
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0 |
0 |
28 |
0 |
0 |
2 |
37 |
High-dimensional Data Bootstrap |
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2 |
2 |
38 |
1 |
2 |
3 |
24 |
High-dimensional econometrics and regularized GMM |
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0 |
0 |
13 |
1 |
2 |
9 |
84 |
Honest confidence regions for a regression parameter in logistic regression with a large number of controls |
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0 |
0 |
0 |
1 |
2 |
5 |
7 |
Honest confidence regions for a regression parameter in logistic regression with a large number of controls |
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0 |
1 |
71 |
0 |
1 |
6 |
190 |
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study |
0 |
0 |
3 |
10 |
0 |
0 |
4 |
10 |
IMPROVING ESTIMATES OF MONOTONE FUNCTIONS BY REARRANGEMENT |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
149 |
INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS |
0 |
0 |
0 |
108 |
0 |
1 |
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 |
3 |
175 |
Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
1 |
31 |
0 |
0 |
3 |
117 |
Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
0 |
106 |
0 |
0 |
1 |
323 |
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 |
24 |
0 |
0 |
0 |
7 |
Identification of hedonic equilibrium and nonseparable simultaneous equations |
0 |
0 |
0 |
20 |
0 |
0 |
1 |
53 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
0 |
0 |
0 |
113 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
64 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
Improved Central Limit Theorem and bootstrap approximations in high dimensions |
0 |
0 |
0 |
26 |
0 |
1 |
6 |
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 |
2 |
2 |
20 |
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 |
1 |
1 |
1 |
47 |
Inference for High-Dimensional Sparse Econometric Models |
1 |
1 |
3 |
13 |
2 |
4 |
13 |
79 |
Inference for Low-Rank Models |
0 |
0 |
5 |
46 |
1 |
1 |
9 |
65 |
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 |
1 |
3 |
88 |
Inference for heterogeneous effects using low-rank estimations |
0 |
0 |
1 |
17 |
1 |
3 |
8 |
51 |
Inference for high-dimensional sparse econometric models |
0 |
0 |
0 |
56 |
0 |
0 |
4 |
187 |
Inference in High Dimensional Panel Models with an Application to Gun Control |
0 |
0 |
0 |
7 |
0 |
1 |
6 |
46 |
Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
4 |
Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
25 |
0 |
0 |
2 |
86 |
Inference on Counterfactual Distributions |
0 |
1 |
3 |
23 |
0 |
2 |
7 |
143 |
Inference on Sets in Finance |
0 |
0 |
0 |
13 |
1 |
1 |
4 |
40 |
Inference on Treatment Effects After Selection Amongst High-Dimensional Controls |
0 |
1 |
5 |
9 |
4 |
8 |
20 |
76 |
Inference on average treatment effects in aggregate panel data settings |
0 |
0 |
2 |
40 |
0 |
0 |
3 |
162 |
Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
0 |
14 |
0 |
2 |
2 |
25 |
Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
0 |
14 |
1 |
1 |
3 |
45 |
Inference on counterfactual distributions |
0 |
0 |
0 |
1 |
1 |
3 |
3 |
4 |
Inference on counterfactual distributions |
0 |
0 |
0 |
893 |
0 |
0 |
3 |
1,919 |
Inference on counterfactual distributions |
0 |
0 |
0 |
434 |
0 |
0 |
1 |
937 |
Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
Inference on counterfactual distributions |
0 |
0 |
0 |
113 |
0 |
1 |
3 |
351 |
Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
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 |
0 |
0 |
0 |
0 |
1 |
Inference on sets in finance |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
51 |
Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
1 |
46 |
1 |
2 |
9 |
138 |
Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
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 |
Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework |
0 |
0 |
0 |
12 |
1 |
2 |
2 |
15 |
Insights from optimal pandemic shielding in a multi-group SEIR framework |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
Instrumental Variable Quantile Regression |
0 |
0 |
3 |
56 |
0 |
2 |
10 |
64 |
Intersection Bounds: estimation and inference |
0 |
0 |
1 |
88 |
1 |
1 |
4 |
329 |
Intersection Bounds: estimation and inference |
0 |
0 |
1 |
1 |
0 |
0 |
2 |
5 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
17 |
0 |
0 |
1 |
96 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
36 |
0 |
1 |
2 |
126 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
L1-Penalized Quantile Regression in High-Dimensional Sparse Models |
0 |
0 |
0 |
34 |
0 |
0 |
0 |
121 |
L1-Penalized quantile regression in high-dimensional sparse models |
0 |
0 |
0 |
73 |
0 |
0 |
0 |
270 |
LASSO Methods for Gaussian Instrumental Variables Models |
0 |
0 |
0 |
10 |
0 |
1 |
1 |
48 |
LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
4 |
0 |
1 |
1 |
20 |
LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
23 |
LASSO-Driven Inference in Time and Space |
0 |
0 |
1 |
37 |
0 |
0 |
4 |
97 |
LASSO-Driven Inference in Time and Space |
1 |
3 |
3 |
40 |
1 |
3 |
5 |
84 |
LASSO-driven inference in time and space |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
34 |
Learning and Disagreement in an Uncertain World |
0 |
0 |
0 |
102 |
1 |
2 |
6 |
387 |
Learning and Disagreement in an Uncertain World |
0 |
0 |
1 |
120 |
2 |
4 |
11 |
522 |
Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
49 |
0 |
1 |
2 |
173 |
Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
1 |
13 |
0 |
1 |
4 |
136 |
Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
80 |
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 |
14 |
137 |
Locally Robust Semiparametric Estimation |
0 |
0 |
3 |
26 |
0 |
1 |
6 |
187 |
Locally robust semiparametric estimation |
0 |
0 |
1 |
18 |
0 |
2 |
6 |
93 |
Locally robust semiparametric estimation |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
Locally robust semiparametric estimation |
0 |
0 |
0 |
32 |
0 |
2 |
4 |
167 |
Long Story Short: Omitted Variable Bias in Causal Machine Learning |
1 |
2 |
11 |
188 |
2 |
5 |
30 |
126 |
Long Story Short: Omitted Variable Bias in Causal Machine Learning |
0 |
0 |
3 |
32 |
0 |
3 |
14 |
157 |
Mastering Panel 'Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
0 |
125 |
1 |
1 |
4 |
73 |
Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
0 |
41 |
0 |
0 |
0 |
39 |
Minimax Semiparametric Learning With Approximate Sparsity |
1 |
1 |
1 |
13 |
2 |
3 |
6 |
28 |
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 |
3 |
0 |
1 |
1 |
7 |
Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2 |
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 |
1 |
0 |
1 |
1 |
4 |
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 |
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 |
0 |
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 |
0 |
0 |
0 |
0 |
1 |
Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
23 |
0 |
0 |
1 |
39 |
Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
12 |
0 |
1 |
1 |
59 |
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 |
0 |
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 |
0 |
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 |
0 |
2 |
2 |
80 |
Optimal Targeted Lockdowns in a Multi-Group SIR Model |
0 |
0 |
2 |
113 |
2 |
7 |
24 |
647 |
Parameter Set Inference in a Class of Econometric Models |
0 |
0 |
0 |
1 |
3 |
3 |
10 |
680 |
Philip G. Wright, directed acyclic graphs, and instrumental variables |
7 |
116 |
116 |
116 |
6 |
274 |
274 |
274 |
Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables |
0 |
0 |
0 |
5 |
0 |
0 |
4 |
52 |
Pivotal estimation via square-root lasso in nonparametric regression |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
Pivotal estimation via square-root lasso in nonparametric regression |
0 |
0 |
0 |
17 |
0 |
1 |
2 |
78 |
Plug-in regularized estimation of high dimensional parameters in nonlinear semiparametric models |
0 |
0 |
0 |
38 |
0 |
1 |
1 |
111 |
Policy Learning with Confidence |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Post-Selection Inference for Generalized Linear Models with Many Controls |
0 |
0 |
2 |
17 |
0 |
1 |
3 |
47 |
Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments |
0 |
0 |
1 |
4 |
0 |
1 |
3 |
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 |
0 |
0 |
1 |
3 |
5 |
Post-selection and post-regularization inference in linear models with many controls and instruments |
0 |
0 |
0 |
40 |
1 |
2 |
10 |
152 |
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 |
0 |
0 |
1 |
1 |
2 |
Posterior inference in curved exponential families under increasing dimensions |
0 |
0 |
0 |
2 |
1 |
1 |
2 |
28 |
Program Evaluation and Causal Inference with High-Dimensional Data |
1 |
1 |
1 |
13 |
2 |
2 |
9 |
74 |
Program evaluation and causal inference with high-dimensional data |
0 |
0 |
0 |
1 |
0 |
2 |
5 |
11 |
Program evaluation and causal inference with high-dimensional data |
0 |
0 |
0 |
27 |
1 |
1 |
2 |
121 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
2 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
6 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
5 |
1 |
1 |
2 |
79 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
9 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
75 |
0 |
0 |
0 |
200 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
16 |
1 |
1 |
2 |
121 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
5 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
11 |
1 |
2 |
3 |
90 |
QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING |
0 |
0 |
0 |
71 |
0 |
1 |
2 |
339 |
Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
0 |
2 |
49 |
0 |
1 |
6 |
96 |
Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
0 |
0 |
3 |
1 |
2 |
3 |
45 |
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 |
0 |
4 |
452 |
Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
0 |
0 |
3 |
287 |
0 |
1 |
8 |
945 |
Quantile Regression with Censoring and Endogeneity |
0 |
0 |
2 |
58 |
0 |
0 |
2 |
188 |
Quantile Regression with Censoring and Endogeneity |
0 |
0 |
1 |
112 |
0 |
0 |
2 |
364 |
Quantile Regression with Censoring and Endogeneity |
0 |
1 |
1 |
5 |
1 |
2 |
2 |
109 |
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 |
1 |
1 |
1 |
30 |
Quantile and Probability Curves without Crossing |
0 |
0 |
0 |
18 |
0 |
2 |
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 |
0 |
1 |
272 |
Quantile graphical models: prediction and conditional independence with applications to systemic risk |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
13 |
Quantile graphical models: prediction and conditional independence with applications to systemic risk |
0 |
0 |
0 |
34 |
1 |
3 |
3 |
38 |
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 |
1 |
1 |
4 |
139 |
Quantreg.nonpar: an R package for performing nonparametric series quantile regression |
0 |
0 |
0 |
19 |
1 |
1 |
3 |
131 |
Quantreg.nonpar: an R package for performing nonparametric series quantile regression |
0 |
0 |
2 |
3 |
0 |
2 |
9 |
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 |
0 |
1 |
Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
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 |
0 |
3 |
60 |
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests |
0 |
0 |
1 |
34 |
1 |
3 |
5 |
45 |
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 |
0 |
0 |
0 |
24 |
Semi-Parametric Efficient Policy Learning with Continuous Actions |
0 |
0 |
0 |
7 |
0 |
2 |
2 |
15 |
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 |
20 |
0 |
0 |
0 |
90 |
Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
35 |
Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
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 |
1 |
1 |
4 |
71 |
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 |
0 |
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 |
0 |
3 |
8 |
214 |
Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models |
0 |
0 |
0 |
15 |
0 |
0 |
2 |
42 |
Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
24 |
Single market non-parametric identification of multi-attribute hedonic equilibrium models |
0 |
0 |
0 |
4 |
0 |
0 |
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 |
1 |
1 |
2 |
30 |
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 |
0 |
1 |
26 |
Subvector inference in PI models with many moment inequalities |
0 |
0 |
0 |
20 |
1 |
3 |
5 |
17 |
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 |
0 |
1 |
1 |
13 |
0 |
1 |
2 |
79 |
Testing many moment inequalities |
0 |
1 |
1 |
2 |
0 |
1 |
2 |
3 |
Testing many moment inequalities |
0 |
0 |
0 |
15 |
0 |
0 |
0 |
38 |
Testing many moment inequalities |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Testing many moment inequalities |
0 |
0 |
1 |
35 |
0 |
0 |
2 |
89 |
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 |
0 |
18 |
188 |
1 |
4 |
44 |
385 |
The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
0 |
1 |
15 |
1 |
2 |
7 |
64 |
The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
7 |
The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
14 |
0 |
1 |
2 |
79 |
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 |
0 |
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 |
0 |
0 |
0 |
1 |
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 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 |
1 |
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 |
0 |
2 |
48 |
Valid post-selection and post-regularization inference: An elementary, general approach |
0 |
0 |
0 |
0 |
0 |
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 |
2 |
3 |
4 |
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 |
0 |
1 |
36 |
Vector Quantile Regression |
0 |
0 |
0 |
3 |
0 |
3 |
5 |
51 |
Vector Quantile Regression: An Optimal Transport Approach |
1 |
1 |
2 |
26 |
1 |
1 |
3 |
86 |
Vector Quantile Regression: An Optimal Transport Approach |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
Vector quantile regression |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
Vector quantile regression |
0 |
0 |
0 |
9 |
0 |
0 |
0 |
44 |
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 |
0 |
0 |
0 |
0 |
1 |
Vector quantile regression: an optimal transport approach |
0 |
0 |
0 |
21 |
0 |
0 |
0 |
52 |
Welfare Analysis in Dynamic Models |
0 |
0 |
0 |
17 |
0 |
1 |
6 |
37 |
hdm: High-Dimensional Metrics |
0 |
1 |
2 |
2 |
0 |
2 |
5 |
9 |
hdm: High-Dimensional Metrics |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
35 |
hdm: High-Dimensional Metrics |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
29 |
quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression |
0 |
0 |
0 |
6 |
0 |
4 |
4 |
53 |
Total Working Papers |
44 |
281 |
829 |
10,789 |
188 |
981 |
2,823 |
33,951 |
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 |
2 |
6 |
7 |
ADMISSIBLE INVARIANT SIMILAR TESTS FOR INSTRUMENTAL VARIABLES REGRESSION |
0 |
0 |
0 |
8 |
1 |
1 |
2 |
57 |
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
0 |
1 |
7 |
27 |
1 |
3 |
26 |
71 |
An IV Model of Quantile Treatment Effects |
0 |
1 |
5 |
453 |
0 |
2 |
11 |
1,311 |
An MCMC approach to classical estimation |
0 |
2 |
10 |
538 |
1 |
6 |
26 |
1,146 |
Automatic Debiased Machine Learning of Causal and Structural Effects |
0 |
1 |
7 |
37 |
0 |
3 |
26 |
113 |
Average and Quantile Effects in Nonseparable Panel Models |
0 |
0 |
0 |
38 |
0 |
0 |
6 |
193 |
Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S |
0 |
1 |
3 |
25 |
0 |
2 |
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 |
1 |
1 |
12 |
107 |
Conditional value-at-risk: Aspects of modeling and estimation |
0 |
0 |
1 |
880 |
1 |
1 |
4 |
2,138 |
Constrained Conditional Moment Restriction Models |
0 |
1 |
1 |
4 |
0 |
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 |
1 |
1 |
3 |
9 |
Double/Debiased/Neyman Machine Learning of Treatment Effects |
0 |
0 |
2 |
72 |
1 |
5 |
15 |
282 |
Double/debiased machine learning for treatment and structural parameters |
3 |
6 |
22 |
105 |
11 |
27 |
92 |
400 |
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 |
4 |
6 |
316 |
2 |
6 |
11 |
713 |
Fast algorithms for the quantile regression process |
0 |
1 |
1 |
5 |
0 |
3 |
8 |
25 |
Finite sample inference for quantile regression models |
0 |
0 |
1 |
65 |
0 |
1 |
5 |
286 |
Fragility of asymptotic agreement under Bayesian learning |
0 |
0 |
0 |
47 |
1 |
4 |
7 |
230 |
Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
1 |
1 |
10 |
0 |
2 |
6 |
47 |
High-Dimensional Methods and Inference on Structural and Treatment Effects |
0 |
1 |
6 |
51 |
0 |
5 |
26 |
274 |
Identification of Hedonic Equilibrium and Nonseparable Simultaneous Equations |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
59 |
Implementing intersection bounds in Stata |
0 |
1 |
4 |
42 |
0 |
2 |
7 |
161 |
Improving point and interval estimators of monotone functions by rearrangement |
0 |
0 |
0 |
34 |
0 |
2 |
4 |
138 |
Inference approaches for instrumental variable quantile regression |
0 |
0 |
1 |
462 |
0 |
1 |
12 |
1,176 |
Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
0 |
0 |
1 |
36 |
0 |
2 |
10 |
158 |
Inference in High-Dimensional Panel Models With an Application to Gun Control |
0 |
0 |
6 |
60 |
1 |
4 |
29 |
215 |
Inference on Causal and Structural Parameters using Many Moment Inequalities |
1 |
1 |
2 |
11 |
1 |
3 |
6 |
77 |
Inference on Counterfactual Distributions |
0 |
1 |
2 |
367 |
0 |
2 |
13 |
965 |
Inference on Treatment Effects after Selection among High-Dimensional Controls†|
0 |
3 |
6 |
89 |
1 |
9 |
25 |
316 |
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 |
0 |
1 |
9 |
522 |
0 |
5 |
30 |
1,093 |
Instrumental variable estimation of nonseparable models |
0 |
0 |
0 |
173 |
0 |
0 |
1 |
355 |
Instrumental variable quantile regression: A robust inference approach |
0 |
5 |
28 |
457 |
2 |
11 |
50 |
964 |
Intersection Bounds: Estimation and Inference |
0 |
0 |
0 |
20 |
0 |
0 |
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 |
0 |
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 |
5 |
16 |
52 |
Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
1 |
28 |
0 |
1 |
7 |
86 |
Network and panel quantile effects via distribution regression |
0 |
1 |
4 |
4 |
1 |
4 |
10 |
10 |
Nonparametric identification in panels using quantiles |
0 |
0 |
1 |
16 |
0 |
0 |
1 |
93 |
Nonseparable multinomial choice models in cross-section and panel data |
0 |
1 |
1 |
13 |
0 |
3 |
4 |
65 |
Optimal Targeted Lockdowns in a Multigroup SIR Model |
0 |
2 |
5 |
35 |
2 |
9 |
29 |
189 |
Post-Selection Inference for Generalized Linear Models With Many Controls |
0 |
0 |
2 |
53 |
0 |
0 |
10 |
166 |
Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments |
0 |
0 |
1 |
38 |
1 |
4 |
11 |
229 |
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 |
0 |
1 |
2 |
33 |
1 |
4 |
8 |
140 |
Quantile Models with Endogeneity |
0 |
0 |
1 |
49 |
0 |
1 |
6 |
209 |
Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
0 |
1 |
3 |
400 |
1 |
4 |
8 |
1,240 |
Quantile and Probability Curves Without Crossing |
0 |
0 |
0 |
82 |
0 |
1 |
2 |
307 |
Quantile regression with censoring and endogeneity |
0 |
1 |
6 |
89 |
1 |
4 |
15 |
398 |
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 |
0 |
1 |
2 |
26 |
Set identification and sensitivity analysis with Tobin regressors |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
74 |
Some new asymptotic theory for least squares series: Pointwise and uniform results |
1 |
2 |
8 |
90 |
2 |
6 |
29 |
316 |
Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain |
0 |
0 |
4 |
113 |
0 |
1 |
18 |
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 |
2 |
20 |
235 |
0 |
7 |
45 |
600 |
The Impact of Big Data on Firm Performance: An Empirical Investigation |
0 |
0 |
5 |
90 |
2 |
3 |
20 |
382 |
The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
1 |
1 |
17 |
0 |
2 |
4 |
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 |
2 |
15 |
550 |
Three-Step Censored Quantile Regression and Extramarital Affairs |
0 |
0 |
0 |
66 |
0 |
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 |
3 |
44 |
Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models |
0 |
0 |
1 |
4 |
1 |
2 |
3 |
31 |
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 |
1 |
1 |
1 |
3 |
1 |
1 |
3 |
14 |
Vector quantile regression beyond the specified case |
0 |
0 |
0 |
8 |
0 |
1 |
2 |
29 |
Total Journal Articles |
8 |
48 |
227 |
6,979 |
44 |
191 |
821 |
20,683 |