Working Paper |
File Downloads |
Abstract Views |

Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |

A $t$-test for synthetic controls |
0 |
3 |
8 |
81 |
4 |
9 |
43 |
252 |

A Multi-Risk SIR Model with Optimally Targeted Lockdown |
1 |
1 |
2 |
20 |
2 |
3 |
8 |
69 |

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." |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
53 |

A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees |
0 |
0 |
1 |
41 |
0 |
0 |
4 |
56 |

A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
7 |
0 |
1 |
2 |
43 |

A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
12 |

A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
4 |

A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
36 |

Adversarial Estimation of Riesz Representers |
1 |
3 |
3 |
30 |
2 |
5 |
13 |
55 |

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
0 |
1 |
3 |
62 |
0 |
1 |
7 |
176 |

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
0 |
1 |
2 |
6 |
0 |
2 |
6 |
11 |

An MCMC Approach to Classical Estimation |
0 |
0 |
2 |
32 |
1 |
2 |
12 |
30 |

An exact and robust conformal inference method for counterfactual and synthetic controls |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
61 |

An exact and robust conformal inference method for counterfactual and synthetic controls |
0 |
0 |
1 |
1 |
0 |
0 |
7 |
8 |

Anti-concentration and honest, adaptive confidence bands |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
31 |

Anti-concentration and honest, adaptive confidence bands |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
2 |

Anti-concentration and honest, adaptive confidence bands |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
14 |

Anti-concentration and honest, adaptive confidence bands |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Applied Causal Inference Powered by ML and AI |
1 |
3 |
42 |
42 |
7 |
18 |
34 |
34 |

Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models |
0 |
0 |
22 |
22 |
0 |
6 |
21 |
21 |

Arellano-bond lasso estimator for dynamic linear panel models |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
3 |

Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals |
0 |
0 |
2 |
17 |
0 |
1 |
8 |
28 |

Automatic Debiased Machine Learning of Causal and Structural Effects |
0 |
2 |
8 |
72 |
0 |
5 |
22 |
129 |

Automatic Debiased Machine Learning via Riesz Regression |
0 |
0 |
3 |
49 |
0 |
1 |
14 |
98 |

Average and Quantile Effects in Nonseparable Panel Models |
0 |
0 |
0 |
6 |
0 |
0 |
2 |
34 |

Best Linear Approximations to Set Identified Functions: With an Application to the Gender Wage Gap |
0 |
0 |
0 |
32 |
0 |
0 |
1 |
124 |

Best linear approximations to set identified functions: with an application to the gender wage gap |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
2 |

Best linear approximations to set identified functions: with an application to the gender wage gap |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
54 |

Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
59 |

Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
18 |

Censored Quantile Instrumental Variable Estimation via Control Functions |
0 |
0 |
0 |
40 |
0 |
0 |
1 |
177 |

Censored Quantile Instrumental Variable Estimation with Stata |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
61 |

Censored Quantile Instrumental Variable Estimation with Stata |
0 |
0 |
0 |
13 |
0 |
0 |
2 |
99 |

Censored Quantile Instrumental Variable Estimation with Stata |
0 |
0 |
0 |
11 |
0 |
0 |
1 |
58 |

Central limit theorems and bootstrap in high dimensions |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
41 |

Central limit theorems and bootstrap in high dimensions |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |

Central limit theorems and bootstrap in high dimensions |
0 |
0 |
0 |
21 |
0 |
0 |
2 |
63 |

Central limit theorems and bootstrap in high dimensions |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
2 |

Central limit theorems and multiplier bootstrap when p is much larger than n |
0 |
0 |
3 |
3 |
0 |
1 |
8 |
8 |

Central limit theorems and multiplier bootstrap when p is much larger than n |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
85 |

Closing the U.S. gender wage gap requires understanding its heterogeneity |
0 |
1 |
3 |
68 |
1 |
2 |
7 |
132 |

Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
0 |
0 |
0 |
3 |
0 |
1 |
3 |
46 |

Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
3 |

Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
25 |

Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
0 |
0 |
1 |
1 |
0 |
1 |
8 |
8 |

Conditional Quantile Processes based on Series or Many Regressors |
0 |
0 |
1 |
6 |
1 |
1 |
4 |
81 |

Conditional quantile processes based on series or many regressors |
0 |
0 |
0 |
15 |
0 |
0 |
1 |
48 |

Conditional quantile processes based on series or many regressors |
0 |
0 |
3 |
3 |
0 |
0 |
5 |
5 |

Conditional quantile processes based on series or many regressors |
0 |
0 |
0 |
48 |
0 |
0 |
1 |
111 |

Confidence bands for coefficients in high dimensional linear models with error-in-variables |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
4 |

Confidence bands for coefficients in high dimensional linear models with error-in-variables |
0 |
0 |
0 |
28 |
0 |
0 |
0 |
33 |

Constrained conditional moment restriction models |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
2 |

Constrained conditional moment restriction models |
0 |
0 |
2 |
25 |
0 |
0 |
4 |
87 |

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 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Counterfactual analysis in R: a vignette |
0 |
0 |
0 |
0 |
0 |
0 |
11 |
13 |

Counterfactual analysis in R: a vignette |
1 |
1 |
1 |
53 |
1 |
1 |
4 |
221 |

Counterfactual: An R Package for Counterfactual Analysis |
0 |
0 |
1 |
18 |
0 |
1 |
2 |
77 |

De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers |
0 |
0 |
1 |
72 |
0 |
0 |
6 |
115 |

Demand Analysis with Many Prices |
0 |
0 |
2 |
95 |
1 |
2 |
6 |
130 |

Demand analysis with many prices |
0 |
0 |
1 |
7 |
0 |
2 |
10 |
45 |

Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK |
0 |
1 |
4 |
58 |
1 |
4 |
22 |
125 |

Distribution regression with sample selection and UK wage decomposition |
0 |
1 |
6 |
32 |
0 |
2 |
21 |
33 |

Distribution regression with sample selection, with an application to wage decompositions in the UK |
0 |
0 |
0 |
2 |
1 |
1 |
4 |
37 |

Distributional conformal prediction |
0 |
0 |
1 |
43 |
0 |
0 |
5 |
135 |

Distributional conformal prediction |
0 |
0 |
1 |
2 |
0 |
0 |
2 |
6 |

Double machine learning for treatment and causal parameters |
0 |
1 |
3 |
114 |
1 |
5 |
14 |
510 |

Double machine learning for treatment and causal parameters |
0 |
1 |
2 |
4 |
0 |
1 |
9 |
19 |

Double/Debiased Machine Learning for Treatment and Causal Parameters |
17 |
78 |
360 |
873 |
61 |
210 |
862 |
2,251 |

Double/Debiased Machine Learning for Treatment and Structural Parameters |
1 |
3 |
3 |
114 |
7 |
22 |
48 |
347 |

Double/de-biased machine learning using regularized Riesz representers |
0 |
0 |
4 |
31 |
0 |
1 |
8 |
73 |

Double/debiased machine learning for treatment and structural parameters |
0 |
1 |
1 |
1 |
0 |
1 |
8 |
9 |

Double/debiased machine learning for treatment and structural parameters |
0 |
1 |
4 |
34 |
4 |
7 |
16 |
95 |

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python |
0 |
0 |
4 |
18 |
0 |
0 |
12 |
64 |

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R |
0 |
1 |
4 |
58 |
0 |
1 |
20 |
100 |

DoubleMLDeep: Estimation of Causal Effects with Multimodal Data |
0 |
1 |
17 |
17 |
1 |
10 |
26 |
26 |

Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
23 |

Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments |
1 |
2 |
25 |
25 |
1 |
2 |
13 |
13 |

Estimation of treatment effects with high-dimensional controls |
0 |
0 |
0 |
38 |
0 |
0 |
1 |
73 |

Estimation of treatment effects with high-dimensional controls |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Exact and robust conformal inference methods for predictive machine learning with dependent data |
0 |
0 |
0 |
69 |
0 |
0 |
2 |
67 |

Extremal Quantile Regression: An Overview |
0 |
0 |
1 |
49 |
0 |
0 |
3 |
56 |

Extremal quantile regression |
0 |
0 |
1 |
17 |
0 |
0 |
3 |
59 |

Extremal quantile regression: an overview |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
2 |

Extremal quantile regression: an overview |
0 |
1 |
2 |
6 |
0 |
1 |
3 |
39 |

Fast Algorithms for the Quantile Regression Process |
0 |
0 |
0 |
51 |
0 |
0 |
0 |
112 |

Finite-Sample Inference Methods for Quantile Regression Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
248 |

Fischer-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
0 |
0 |
1 |
1 |
1 |
11 |
102 |
102 |

Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
4 |
7 |
50 |
235 |
7 |
28 |
162 |
642 |

Fragility of Asymptotic Agreement under Bayesian Learning |
0 |
0 |
0 |
88 |
0 |
0 |
2 |
260 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
1 |
6 |
0 |
0 |
5 |
46 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
31 |
0 |
0 |
0 |
65 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
3 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
1 |
5 |
0 |
0 |
3 |
38 |

Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
6 |
9 |
0 |
1 |
16 |
62 |

Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
1 |
1 |
1 |
0 |
3 |
6 |
8 |

Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
0 |
13 |
0 |
0 |
5 |
94 |

Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
0 |
7 |
1 |
1 |
2 |
19 |

Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
0 |
8 |
0 |
0 |
0 |
45 |

Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
0 |
59 |
0 |
0 |
1 |
101 |

Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
0 |
0 |
6 |
94 |
0 |
2 |
18 |
298 |

Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
38 |

Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
50 |

Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
1 |
4 |
59 |
1 |
3 |
13 |
112 |

Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
0 |
0 |
2 |
2 |
4 |
20 |
23 |

Hedonic Prices and Quality Adjusted Price Indices Powered by AI |
1 |
1 |
1 |
16 |
1 |
3 |
5 |
11 |

Hedonic prices and quality adjusted price indices powered by AI |
0 |
1 |
5 |
20 |
1 |
3 |
24 |
38 |

High Dimensional Sparse Econometric Models: An Introduction |
0 |
0 |
1 |
13 |
1 |
2 |
6 |
50 |

High dimensional methods and inference on structural and treatment effects |
0 |
0 |
1 |
1 |
0 |
0 |
6 |
6 |

High dimensional methods and inference on structural and treatment effects |
0 |
0 |
0 |
22 |
0 |
0 |
1 |
109 |

High-Dimensional Econometrics and Regularized GMM |
0 |
2 |
6 |
58 |
2 |
5 |
14 |
155 |

High-Dimensional Metrics in R |
0 |
0 |
1 |
28 |
0 |
1 |
5 |
37 |

High-dimensional Data Bootstrap |
0 |
0 |
2 |
36 |
0 |
0 |
3 |
22 |

High-dimensional econometrics and regularized GMM |
0 |
0 |
0 |
13 |
0 |
2 |
8 |
79 |

Honest confidence regions for a regression parameter in logistic regression with a large number of controls |
1 |
1 |
2 |
71 |
1 |
1 |
7 |
186 |

Honest confidence regions for a regression parameter in logistic regression with a large number of controls |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
4 |

Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study |
0 |
0 |
9 |
9 |
0 |
0 |
8 |
8 |

IMPROVING ESTIMATES OF MONOTONE FUNCTIONS BY REARRANGEMENT |
0 |
0 |
0 |
39 |
0 |
0 |
1 |
149 |

INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS |
0 |
0 |
0 |
108 |
1 |
2 |
3 |
386 |

Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game |
0 |
0 |
0 |
47 |
1 |
1 |
1 |
68 |

Identification and Estimation of Marginal Effects in Nonlinear Panel Models |
0 |
0 |
0 |
47 |
0 |
0 |
4 |
172 |

Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
1 |
106 |
0 |
0 |
2 |
322 |

Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
0 |
30 |
0 |
0 |
3 |
115 |

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 |
1 |
7 |

Identification of hedonic equilibrium and nonseparable simultaneous equations |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
52 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
0 |
0 |
1 |
113 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
0 |
0 |
3 |
64 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
4 |

Improved Central Limit Theorem and bootstrap approximations in high dimensions |
0 |
0 |
1 |
26 |
0 |
0 |
11 |
92 |

Improving Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
16 |

Improving Point and Interval Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
18 |

Improving estimates of monotone functions by rearrangement |
0 |
0 |
0 |
58 |
0 |
0 |
1 |
226 |

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 |
2 |

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 |
1 |
1 |
11 |
0 |
2 |
8 |
72 |

Inference for Low-Rank Models |
0 |
2 |
7 |
44 |
0 |
4 |
22 |
61 |

Inference for best linear approximations to set identified functions |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Inference for best linear approximations to set identified functions |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
108 |

Inference for extremal conditional quantile models, with an application to market and birthweight risks |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
85 |

Inference for heterogeneous effects using low-rank estimations |
0 |
1 |
1 |
17 |
0 |
3 |
4 |
46 |

Inference for high-dimensional sparse econometric models |
0 |
0 |
1 |
56 |
0 |
0 |
4 |
185 |

Inference in High Dimensional Panel Models with an Application to Gun Control |
0 |
0 |
1 |
7 |
0 |
0 |
4 |
41 |

Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
25 |
0 |
0 |
1 |
84 |

Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
2 |

Inference on Counterfactual Distributions |
1 |
1 |
1 |
21 |
1 |
2 |
6 |
138 |

Inference on Sets in Finance |
0 |
0 |
1 |
13 |
0 |
0 |
2 |
36 |

Inference on Treatment Effects After Selection Amongst High-Dimensional Controls |
1 |
2 |
4 |
7 |
1 |
3 |
9 |
62 |

Inference on average treatment effects in aggregate panel data settings |
1 |
1 |
1 |
39 |
1 |
1 |
7 |
161 |

Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
2 |
14 |
0 |
0 |
4 |
23 |

Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
0 |
14 |
0 |
1 |
1 |
43 |

Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Inference on counterfactual distributions |
0 |
0 |
0 |
434 |
0 |
1 |
2 |
937 |

Inference on counterfactual distributions |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |

Inference on counterfactual distributions |
0 |
0 |
0 |
113 |
1 |
1 |
4 |
349 |

Inference on counterfactual distributions |
0 |
0 |
0 |
893 |
0 |
1 |
3 |
1,918 |

Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Inference on sets in finance |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
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 |
1 |
1 |

Inference on sets in finance |
0 |
0 |
0 |
70 |
0 |
0 |
2 |
165 |

Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
0 |
0 |
0 |
6 |
6 |

Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
14 |
0 |
1 |
2 |
101 |

Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
45 |
0 |
2 |
9 |
133 |

Inference on weighted average value function in high-dimensional state space |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
31 |

Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
13 |

Insights from optimal pandemic shielding in a multi-group SEIR framework |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Instrumental Variable Quantile Regression |
0 |
1 |
3 |
55 |
0 |
1 |
7 |
56 |

Intersection Bounds: estimation and inference |
0 |
0 |
0 |
87 |
0 |
1 |
5 |
327 |

Intersection Bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
4 |

Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Intersection bounds: estimation and inference |
0 |
0 |
0 |
36 |
0 |
0 |
2 |
124 |

Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
3 |

Intersection bounds: estimation and inference |
0 |
0 |
0 |
17 |
0 |
0 |
3 |
96 |

L1-Penalized Quantile Regression in High-Dimensional Sparse Models |
0 |
0 |
6 |
34 |
0 |
0 |
19 |
121 |

L1-Penalized quantile regression in high-dimensional sparse models |
0 |
0 |
0 |
73 |
0 |
0 |
3 |
270 |

LASSO Methods for Gaussian Instrumental Variables Models |
0 |
0 |
1 |
10 |
0 |
0 |
2 |
47 |

LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
36 |
0 |
1 |
4 |
94 |

LASSO-Driven Inference in Time and Space |
0 |
0 |
2 |
4 |
0 |
0 |
7 |
19 |

LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
23 |

LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
37 |
0 |
0 |
4 |
81 |

LASSO-driven inference in time and space |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
33 |

Learning and Disagreement in an Uncertain World |
0 |
0 |
2 |
120 |
0 |
2 |
5 |
515 |

Learning and Disagreement in an Uncertain World |
0 |
0 |
0 |
102 |
1 |
1 |
1 |
382 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
12 |
0 |
1 |
3 |
134 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
49 |
0 |
0 |
2 |
171 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
80 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
31 |
1 |
3 |
6 |
128 |

Locally Robust Semiparametric Estimation |
0 |
0 |
3 |
26 |
1 |
1 |
5 |
185 |

Locally robust semiparametric estimation |
0 |
0 |
0 |
17 |
0 |
0 |
4 |
88 |

Locally robust semiparametric estimation |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |

Locally robust semiparametric estimation |
0 |
0 |
0 |
32 |
0 |
0 |
1 |
163 |

Long Story Short: Omitted Variable Bias in Causal Machine Learning |
1 |
2 |
8 |
182 |
3 |
5 |
40 |
112 |

Long Story Short: Omitted Variable Bias in Causal Machine Learning |
1 |
1 |
4 |
31 |
1 |
2 |
13 |
149 |

Mastering Panel 'Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
0 |
125 |
0 |
0 |
1 |
69 |

Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
0 |
41 |
0 |
0 |
1 |
39 |

Minimax Semiparametric Learning With Approximate Sparsity |
0 |
0 |
2 |
12 |
0 |
2 |
7 |
24 |

Monge-Kantorovich Depth, Quantiles, Ranks and Signs |
0 |
0 |
1 |
40 |
0 |
0 |
3 |
105 |

Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
1 |
2 |
0 |
0 |
2 |
41 |

Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
3 |
4 |
0 |
0 |
7 |
52 |

Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
1 |
1 |
0 |
0 |
2 |
2 |

Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
1 |
3 |
0 |
0 |
1 |
6 |

Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
1 |
1 |
0 |
0 |
3 |
3 |

Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
1 |
1 |
0 |
0 |
2 |
2 |

Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
1 |
6 |
0 |
0 |
3 |
76 |

Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
1 |
1 |
0 |
2 |
4 |
4 |

Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
1 |
9 |
0 |
0 |
2 |
52 |

Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
3 |
5 |
0 |
0 |
3 |
12 |

Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
1 |
50 |
0 |
2 |
3 |
97 |

Network and panel quantile effects via distribution regression |
0 |
0 |
0 |
11 |
0 |
0 |
3 |
29 |

Network and panel quantile effects via distribution regression |
0 |
0 |
2 |
2 |
0 |
0 |
3 |
22 |

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 |
1 |
169 |

Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
23 |
0 |
0 |
0 |
38 |

Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
58 |

Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

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 |
15 |
0 |
0 |
1 |
24 |

Nonseparable multinomial choice models in cross-section and panel data |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

On the asymptotic theory for least squares series: pointwise and uniform results |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |

On the asymptotic theory for least squares series: pointwise and uniform results |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
65 |

On the computational complexity of MCMC-based estimators in large samples |
0 |
0 |
0 |
20 |
0 |
0 |
2 |
78 |

Optimal Targeted Lockdowns in a Multi-Group SIR Model |
0 |
1 |
2 |
112 |
4 |
7 |
19 |
632 |

Parameter Set Inference in a Class of Econometric Models |
0 |
0 |
0 |
1 |
0 |
2 |
9 |
674 |

Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables |
0 |
0 |
1 |
5 |
1 |
1 |
4 |
50 |

Pivotal estimation via square-root lasso in nonparametric regression |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Pivotal estimation via square-root lasso in nonparametric regression |
0 |
0 |
0 |
17 |
0 |
0 |
1 |
76 |

Plug-in regularized estimation of high dimensional parameters in nonlinear semiparametric models |
0 |
0 |
0 |
38 |
0 |
0 |
2 |
110 |

Post-Selection Inference for Generalized Linear Models with Many Controls |
0 |
0 |
3 |
15 |
0 |
0 |
4 |
44 |

Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
32 |

Post-l1-penalized estimators in high-dimensional linear regression models |
0 |
0 |
0 |
50 |
0 |
0 |
0 |
164 |

Post-selection and post-regularization inference in linear models with many controls and instruments |
0 |
0 |
0 |
40 |
6 |
6 |
8 |
148 |

Post-selection and post-regularization inference in linear models with many controls and instruments |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Posterior Inference in Curved Exponential Families under Increasing Dimensions |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
5 |

Posterior inference in curved exponential families under increasing dimensions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Posterior inference in curved exponential families under increasing dimensions |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
26 |

Program Evaluation and Causal Inference with High-Dimensional Data |
0 |
0 |
1 |
12 |
0 |
3 |
8 |
69 |

Program evaluation and causal inference with high-dimensional data |
0 |
0 |
1 |
27 |
0 |
0 |
1 |
119 |

Program evaluation and causal inference with high-dimensional data |
0 |
0 |
1 |
1 |
2 |
2 |
8 |
8 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
7 |
8 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
119 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
3 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
75 |
0 |
0 |
2 |
200 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
11 |
0 |
0 |
1 |
87 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
77 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING |
0 |
0 |
0 |
71 |
0 |
0 |
0 |
337 |

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
0 |
0 |
3 |
0 |
0 |
2 |
43 |

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
1 |
1 |
48 |
2 |
4 |
4 |
94 |

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk |
0 |
0 |
1 |
20 |
0 |
0 |
2 |
46 |

Quantile Models with Endogeneity |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
54 |

Quantile Regression under Misspecification |
0 |
0 |
0 |
2 |
0 |
1 |
6 |
449 |

Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
0 |
1 |
1 |
285 |
0 |
1 |
6 |
940 |

Quantile Regression with Censoring and Endogeneity |
0 |
0 |
0 |
111 |
0 |
0 |
0 |
362 |

Quantile Regression with Censoring and Endogeneity |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
107 |

Quantile Regression with Censoring and Endogeneity |
0 |
0 |
3 |
57 |
0 |
0 |
8 |
187 |

Quantile and Average Effects in Nonseparable Panel Models |
0 |
0 |
0 |
25 |
0 |
0 |
1 |
99 |

Quantile and Probability Curves Without Crossing |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
29 |

Quantile and Probability Curves without Crossing |
0 |
0 |
0 |
17 |
0 |
1 |
5 |
130 |

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 |
0 |
112 |

Quantile and probability curves without crossing |
0 |
0 |
0 |
68 |
0 |
0 |
1 |
271 |

Quantile graphical models: prediction and conditional independence with applications to systemic risk |
0 |
0 |
0 |
34 |
0 |
0 |
0 |
35 |

Quantile graphical models: prediction and conditional independence with applications to systemic risk |
0 |
0 |
0 |
0 |
1 |
1 |
6 |
11 |

Quantile models with endogeneity |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Quantile models with endogeneity |
0 |
0 |
0 |
89 |
0 |
0 |
4 |
240 |

Quantile regression with censoring and endogeneity |
0 |
0 |
0 |
40 |
1 |
2 |
2 |
137 |

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 |
1 |
3 |
3 |
0 |
3 |
12 |
16 |

Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
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 |
1 |
331 |

Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models |
1 |
1 |
1 |
36 |
1 |
1 |
3 |
58 |

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests |
0 |
1 |
2 |
34 |
0 |
1 |
6 |
41 |

Robust inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |

Robust inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
19 |
0 |
0 |
0 |
102 |

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 |
0 |
0 |
13 |

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 |
0 |
1 |
1 |

Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
35 |

Set identification with Tobin regressors |
0 |
1 |
1 |
64 |
0 |
1 |
2 |
178 |

Shape-Enforcing Operators for Point and Interval Estimators |
0 |
0 |
0 |
30 |
0 |
1 |
4 |
68 |

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 |
0 |
0 |
0 |
1 |
1 |

Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
22 |

Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions |
0 |
0 |
1 |
93 |
0 |
1 |
10 |
209 |

Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models |
0 |
0 |
0 |
15 |
0 |
0 |
1 |
40 |

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 |
0 |
16 |

Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
42 |

SortedEffects: Sorted Causal Effects in R |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
28 |

Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain |
0 |
0 |
2 |
17 |
0 |
2 |
7 |
71 |

Sparse models and methods for optimal instruments with an application to eminent domain |
0 |
0 |
0 |
43 |
0 |
1 |
4 |
155 |

Subvector Inference in Partially Identified Models with Many Moment Inequalities |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
25 |

Subvector inference in PI models with many moment inequalities |
0 |
0 |
0 |
20 |
0 |
0 |
1 |
13 |

Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls" |
0 |
0 |
1 |
2 |
0 |
1 |
8 |
21 |

Testing Many Moment Inequalities |
0 |
0 |
0 |
12 |
0 |
1 |
2 |
78 |

Testing Many Moment Inequalities |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Testing many moment inequalities |
0 |
0 |
1 |
1 |
0 |
1 |
2 |
2 |

Testing many moment inequalities |
0 |
0 |
1 |
35 |
0 |
1 |
6 |
89 |

Testing many moment inequalities |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Testing many moment inequalities |
0 |
0 |
0 |
15 |
0 |
0 |
1 |
38 |

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 |
3 |
22 |
178 |
2 |
8 |
54 |
360 |

The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
1 |
2 |
15 |
0 |
2 |
9 |
59 |

The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
5 |

The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
14 |
0 |
0 |
0 |
77 |

Toward personalized inference on individual treatment effects |
1 |
1 |
2 |
2 |
1 |
1 |
2 |
2 |

Uniform Inference in High-Dimensional Gaussian Graphical Models |
0 |
0 |
0 |
31 |
0 |
0 |
1 |
39 |

Uniform Inference on High-dimensional Spatial Panel Networks |
0 |
0 |
0 |
12 |
0 |
0 |
3 |
51 |

Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
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 |
0 |
0 |
0 |
2 |
2 |

Uniform post selection inference for LAD regression and other Z-estimation problems |
0 |
0 |
0 |
18 |
0 |
0 |
2 |
44 |

Uniform post selection inference for LAD regression and other z-estimation problems |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Uniform post selection inference for LAD regression and other z-estimation problems |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
74 |

Uniform post selection inference for LAD regression models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Uniform post selection inference for LAD regression models |
0 |
0 |
0 |
31 |
0 |
0 |
2 |
96 |

Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models |
0 |
2 |
4 |
12 |
0 |
3 |
8 |
58 |

Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach |
0 |
0 |
1 |
3 |
0 |
0 |
3 |
24 |

Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R) |
0 |
0 |
1 |
22 |
0 |
0 |
2 |
47 |

Valid post-selection and post-regularization inference: An elementary, general approach |
0 |
0 |
0 |
22 |
0 |
0 |
1 |
44 |

Valid post-selection and post-regularization inference: An elementary, general approach |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Valid post-selection inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
55 |

Valid post-selection inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Valid simultaneous inference in high-dimensional settings (with the HDM package for R) |
0 |
0 |
0 |
10 |
0 |
0 |
1 |
35 |

Vector Quantile Regression |
0 |
0 |
0 |
3 |
0 |
2 |
2 |
48 |

Vector Quantile Regression: An Optimal Transport Approach |
0 |
1 |
1 |
25 |
0 |
1 |
5 |
84 |

Vector Quantile Regression: An Optimal Transport Approach |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |

Vector quantile regression |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

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 |
21 |
0 |
0 |
1 |
52 |

Vector quantile regression: an optimal transport approach |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

hdm: High-Dimensional Metrics |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
29 |

hdm: High-Dimensional Metrics |
0 |
0 |
0 |
7 |
0 |
0 |
2 |
35 |

hdm: High-Dimensional Metrics |
0 |
0 |
1 |
1 |
0 |
0 |
7 |
7 |

quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
49 |

Total Working Papers |
36 |
149 |
794 |
10,287 |
156 |
527 |
2,584 |
32,085 |