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Last month |
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A lava attack on the recovery of sums of dense and sparse signals |
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6 |
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1 |
6 |
31 |

A lava attack on the recovery of sums of dense and sparse signals |
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0 |
0 |
0 |
0 |
4 |
5 |

A lava attack on the recovery of sums of dense and sparse signals |
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0 |
3 |
2 |
2 |
7 |
33 |

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls |
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4 |
42 |
6 |
9 |
37 |
90 |

An exact and robust conformal inference method for counterfactual and synthetic controls |
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0 |
2 |
1 |
2 |
11 |
25 |

Anti-concentration and honest, adaptive confidence bands |
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4 |
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1 |
4 |
11 |

Anti-concentration and honest, adaptive confidence bands |
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0 |
0 |
0 |
2 |
2 |
5 |
26 |

Automatic Debiased Machine Learning of Causal and Structural Effects |
1 |
1 |
1 |
37 |
3 |
4 |
12 |
26 |

Average and Quantile Effects in Nonseparable Panel Models |
1 |
1 |
1 |
2 |
2 |
6 |
10 |
15 |

Best Linear Approximations to Set Identified Functions: With an Application to the Gender Wage Gap |
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1 |
29 |
3 |
10 |
22 |
89 |

Best linear approximations to set identified functions: with an application to the gender wage gap |
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0 |
0 |
0 |
1 |
4 |
8 |
8 |

Censored Quantile Instrumental Variable Estimation via Control Functions |
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0 |
0 |
40 |
0 |
2 |
8 |
171 |

Censored Quantile Instrumental Variable Estimation with Stata |
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0 |
0 |
12 |
2 |
7 |
23 |
74 |

Censored Quantile Instrumental Variable Estimation with Stata |
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0 |
1 |
9 |
0 |
5 |
21 |
40 |

Censored Quantile Instrumental Variable Estimation with Stata |
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2 |
5 |
7 |
2 |
8 |
25 |
49 |

Central limit theorems and bootstrap in high dimensions |
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0 |
0 |
2 |
1 |
2 |
11 |
30 |

Central limit theorems and bootstrap in high dimensions |
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0 |
2 |
20 |
2 |
3 |
8 |
51 |

Central limit theorems and multiplier bootstrap when p is much larger than n |
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0 |
1 |
35 |
1 |
1 |
4 |
70 |

Closing the U.S. gender wage gap requires understanding its heterogeneity |
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14 |
44 |
4 |
8 |
49 |
70 |

Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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1 |
1 |
1 |
5 |
13 |

Comparison and anti-concentration bounds for maxima of Gaussian random vectors |
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0 |
0 |
3 |
0 |
0 |
3 |
31 |

Conditional Quantile Processes based on Series or Many Regressors |
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0 |
1 |
4 |
1 |
5 |
15 |
56 |

Conditional quantile processes based on series or many regressors |
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0 |
0 |
47 |
1 |
6 |
16 |
106 |

Conditional quantile processes based on series or many regressors |
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0 |
0 |
14 |
1 |
3 |
16 |
42 |

Confidence bands for coefficients in high dimensional linear models with error-in-variables |
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1 |
27 |
2 |
4 |
8 |
22 |

Constrained conditional moment restriction models |
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1 |
21 |
1 |
6 |
14 |
63 |

Counterfactual analysis in R: a vignette |
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0 |
16 |
32 |
6 |
26 |
89 |
132 |

Counterfactual: An R Package for Counterfactual Analysis |
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11 |
6 |
9 |
29 |
45 |

De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers |
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3 |
56 |
2 |
4 |
20 |
42 |

Demand Analysis with Many Prices |
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2 |
84 |
84 |
1 |
8 |
71 |
71 |

Demand analysis with many prices |
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3 |
3 |
2 |
7 |
13 |
13 |

Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK |
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0 |
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32 |
0 |
5 |
17 |
38 |

Distribution regression with sample selection, with an application to wage decompositions in the UK |
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0 |
2 |
2 |
1 |
3 |
13 |
16 |

Distributional conformal prediction |
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0 |
29 |
29 |
3 |
6 |
53 |
53 |

Double machine learning for treatment and causal parameters |
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5 |
22 |
76 |
15 |
69 |
184 |
298 |

Double/Debiased Machine Learning for Treatment and Causal Parameters |
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32 |
37 |
122 |
36 |
84 |
110 |
219 |

Double/Debiased Machine Learning for Treatment and Structural Parameters |
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0 |
10 |
80 |
1 |
4 |
43 |
134 |

Double/de-biased machine learning using regularized Riesz representers |
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0 |
0 |
15 |
1 |
4 |
13 |
25 |

Double/debiased machine learning for treatment and structural parameters |
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0 |
0 |
23 |
1 |
6 |
20 |
41 |

Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings |
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0 |
0 |
6 |
1 |
1 |
7 |
17 |

Estimation of treatment effects with high-dimensional controls |
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0 |
1 |
37 |
2 |
2 |
7 |
65 |

Exact and robust conformal inference methods for predictive machine learning with dependent data |
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0 |
4 |
66 |
3 |
3 |
14 |
35 |

Extremal Quantile Regression: An Overview |
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2 |
40 |
0 |
3 |
14 |
35 |

Extremal quantile regression |
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0 |
2 |
6 |
0 |
2 |
11 |
23 |

Extremal quantile regression: an overview |
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0 |
0 |
2 |
0 |
2 |
7 |
19 |

Fast Algorithms for the Quantile Regression Process |
3 |
9 |
37 |
37 |
11 |
29 |
84 |
84 |

Finite-Sample Inference Methods for Quantile Regression Models |
0 |
0 |
0 |
0 |
1 |
1 |
7 |
242 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
2 |
1 |
6 |
17 |
29 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
2 |
31 |
2 |
4 |
10 |
56 |

Gaussian approximation of suprema of empirical processes |
0 |
0 |
0 |
4 |
2 |
4 |
7 |
32 |

Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
1 |
1 |
1 |
1 |
11 |
14 |

Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors |
0 |
0 |
3 |
10 |
1 |
2 |
12 |
59 |

Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
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0 |
0 |
4 |
2 |
6 |
18 |
36 |

Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
1 |
1 |
1 |
53 |
3 |
8 |
18 |
85 |

Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments |
1 |
5 |
12 |
137 |
7 |
21 |
65 |
142 |

Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments |
0 |
1 |
19 |
70 |
5 |
14 |
97 |
196 |

Generic inference on quantile and quantile effect functions for discrete outcomes |
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0 |
0 |
3 |
2 |
7 |
20 |
40 |

Generic inference on quantile and quantile effect functions for discrete outcomes |
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0 |
0 |
2 |
1 |
5 |
16 |
31 |

Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
0 |
1 |
44 |
2 |
7 |
28 |
49 |

High Dimensional Sparse Econometric Models: An Introduction |
1 |
2 |
3 |
6 |
2 |
6 |
13 |
25 |

High dimensional methods and inference on structural and treatment effects |
0 |
0 |
3 |
21 |
4 |
5 |
15 |
48 |

High-Dimensional Econometrics and Regularized GMM |
3 |
5 |
9 |
44 |
4 |
13 |
45 |
78 |

High-Dimensional Metrics in R |
0 |
0 |
1 |
25 |
0 |
1 |
3 |
21 |

High-dimensional econometrics and regularized GMM |
0 |
0 |
4 |
11 |
2 |
4 |
16 |
42 |

Honest confidence regions for a regression parameter in logistic regression with a large number of controls |
3 |
6 |
16 |
47 |
3 |
8 |
31 |
127 |

IMPROVING ESTIMATES OF MONOTONE FUNCTIONS BY REARRANGEMENT |
0 |
0 |
0 |
39 |
0 |
2 |
5 |
138 |

INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS |
0 |
0 |
0 |
108 |
5 |
14 |
37 |
287 |

Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game |
0 |
2 |
2 |
41 |
2 |
4 |
4 |
54 |

Identification and Estimation of Marginal Effects in Nonlinear Panel Models |
0 |
0 |
0 |
47 |
1 |
6 |
12 |
147 |

Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
0 |
105 |
1 |
4 |
9 |
313 |

Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
0 |
29 |
0 |
3 |
8 |
102 |

Identification of hedonic equilibrium and nonseparable simultaneous equations |
0 |
1 |
11 |
16 |
2 |
5 |
29 |
37 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
2 |
2 |
12 |
102 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
1 |
1 |
7 |
50 |

Improved Central Limit Theorem and bootstrap approximations in high dimensions |
1 |
2 |
12 |
12 |
1 |
3 |
10 |
10 |

Improving Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
10 |

Improving Point and Interval Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
2 |
0 |
2 |
6 |
12 |

Improving estimates of monotone functions by rearrangement |
0 |
0 |
0 |
56 |
0 |
2 |
8 |
218 |

Improving point and interval estimates of monotone functions by rearrangement |
0 |
0 |
0 |
65 |
0 |
2 |
7 |
308 |

Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
0 |
0 |
0 |
4 |
1 |
3 |
9 |
38 |

Inference for High-Dimensional Sparse Econometric Models |
0 |
0 |
0 |
4 |
0 |
6 |
18 |
37 |

Inference for best linear approximations to set identified functions |
0 |
0 |
0 |
20 |
2 |
5 |
11 |
98 |

Inference for extremal conditional quantile models, with an application to market and birthweight risks |
1 |
1 |
1 |
20 |
1 |
4 |
9 |
70 |

Inference for heterogeneous effects using low-rank estimations |
0 |
0 |
9 |
9 |
2 |
5 |
13 |
13 |

Inference for high-dimensional sparse econometric models |
0 |
0 |
3 |
52 |
3 |
8 |
15 |
157 |

Inference in High Dimensional Panel Models with an Application to Gun Control |
0 |
0 |
1 |
2 |
0 |
2 |
12 |
18 |

Inference in high dimensional panel models with an application to gun control |
1 |
1 |
1 |
23 |
2 |
3 |
7 |
70 |

Inference on Counterfactual Distributions |
0 |
1 |
4 |
5 |
5 |
13 |
42 |
57 |

Inference on Sets in Finance |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
33 |

Inference on Treatment Effects After Selection Amongst High-Dimensional Controls |
0 |
0 |
0 |
1 |
3 |
6 |
18 |
27 |

Inference on average treatment effects in aggregate panel data settings |
1 |
5 |
29 |
29 |
7 |
16 |
33 |
33 |

Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
0 |
11 |
1 |
1 |
7 |
33 |

Inference on causal and structural parameters using many moment inequalities |
0 |
0 |
0 |
11 |
1 |
1 |
1 |
6 |

Inference on counterfactual distributions |
0 |
0 |
2 |
431 |
3 |
10 |
28 |
893 |

Inference on counterfactual distributions |
0 |
1 |
2 |
106 |
3 |
10 |
30 |
242 |

Inference on counterfactual distributions |
0 |
1 |
5 |
887 |
3 |
8 |
27 |
1,802 |

Inference on sets in finance |
0 |
0 |
0 |
3 |
1 |
1 |
5 |
46 |

Inference on sets in finance |
0 |
0 |
0 |
70 |
0 |
1 |
6 |
159 |

Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
1 |
8 |
3 |
4 |
15 |
78 |

Inference on treatment effects after selection amongst high-dimensional controls |
0 |
1 |
2 |
41 |
3 |
6 |
17 |
99 |

Inference on weighted average value function in high-dimensional state space |
0 |
0 |
15 |
15 |
1 |
1 |
15 |
15 |

Intersection Bounds: estimation and inference |
0 |
0 |
0 |
84 |
1 |
2 |
9 |
313 |

Intersection bounds: estimation and inference |
0 |
0 |
1 |
34 |
0 |
2 |
8 |
112 |

Intersection bounds: estimation and inference |
0 |
0 |
0 |
14 |
0 |
0 |
8 |
86 |

L1-Penalized Quantile Regression in High-Dimensional Sparse Models |
0 |
0 |
0 |
2 |
0 |
3 |
13 |
17 |

L1-Penalized quantile regression in high-dimensional sparse models |
1 |
3 |
6 |
68 |
3 |
11 |
23 |
233 |

LASSO Methods for Gaussian Instrumental Variables Models |
0 |
1 |
2 |
4 |
2 |
5 |
15 |
30 |

LASSO-Driven Inference in Time and Space |
0 |
0 |
2 |
35 |
4 |
5 |
18 |
57 |

LASSO-Driven Inference in Time and Space |
0 |
0 |
0 |
0 |
1 |
3 |
7 |
7 |

LASSO-Driven Inference in Time and Space |
0 |
0 |
6 |
28 |
0 |
1 |
22 |
48 |

LASSO-driven inference in time and space |
1 |
1 |
2 |
4 |
2 |
3 |
12 |
19 |

Learning and Disagreement in an Uncertain World |
0 |
1 |
3 |
96 |
1 |
2 |
19 |
357 |

Learning and Disagreement in an Uncertain World |
0 |
0 |
3 |
117 |
1 |
2 |
16 |
490 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
12 |
0 |
1 |
11 |
127 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
48 |
0 |
1 |
4 |
166 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
1 |
2 |
8 |
73 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
1 |
31 |
1 |
3 |
11 |
107 |

Locally Robust Semiparametric Estimation |
0 |
0 |
2 |
9 |
5 |
12 |
37 |
53 |

Locally robust semiparametric estimation |
0 |
0 |
1 |
28 |
2 |
7 |
28 |
80 |

Locally robust semiparametric estimation |
1 |
2 |
5 |
8 |
2 |
5 |
26 |
43 |

Mastering Panel 'Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
8 |
118 |
1 |
5 |
22 |
51 |

Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
38 |
38 |
0 |
3 |
21 |
21 |

Minimax Semiparametric Learning With Approximate Sparsity |
0 |
0 |
6 |
6 |
0 |
1 |
4 |
4 |

Monge-Kantorovich Depth, Quantiles, Ranks and Signs |
0 |
2 |
2 |
39 |
1 |
5 |
21 |
90 |

Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
1 |
1 |
1 |
2 |
5 |
16 |
35 |

Monge-Kantorovich Depth, Quantiles, Ranks, and Signs |
0 |
0 |
0 |
1 |
1 |
3 |
17 |
36 |

Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
0 |
5 |
1 |
3 |
17 |
52 |

Monge-Kantorovich depth, quantiles, ranks and signs |
0 |
0 |
0 |
8 |
1 |
4 |
14 |
46 |

Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
0 |
48 |
4 |
9 |
20 |
72 |

Network and panel quantile effects via distribution regression |
0 |
0 |
1 |
11 |
2 |
4 |
11 |
20 |

Network and panel quantile effects via distribution regression |
0 |
0 |
0 |
0 |
1 |
4 |
9 |
13 |

Nonparametric Identification in Panels using Quantiles |
0 |
0 |
0 |
0 |
0 |
2 |
7 |
8 |

Nonparametric Instrumental Variable Estimators of Structural Quantile Effects |
0 |
1 |
1 |
59 |
1 |
2 |
6 |
158 |

Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
23 |
1 |
3 |
6 |
35 |

Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
12 |
1 |
3 |
6 |
56 |

Nonseparable Multinomial Choice Models in Cross-Section and Panel Data |
0 |
0 |
0 |
42 |
2 |
4 |
8 |
17 |

Nonseparable multinomial choice models in cross-section and panel data |
0 |
0 |
1 |
15 |
2 |
4 |
8 |
18 |

On the asymptotic theory for least squares series: pointwise and uniform results |
0 |
0 |
0 |
16 |
1 |
3 |
7 |
56 |

On the computational complexity of MCMC-based estimators in large samples |
0 |
0 |
0 |
19 |
0 |
0 |
4 |
72 |

Optimal Targeted Lockdowns in a Multi-Group SIR Model |
5 |
25 |
43 |
43 |
28 |
125 |
228 |
228 |

Parameter Set Inference in a Class of Econometric Models |
0 |
0 |
0 |
1 |
0 |
4 |
20 |
641 |

Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables |
0 |
0 |
2 |
3 |
0 |
2 |
12 |
24 |

Pivotal estimation via square-root lasso in nonparametric regression |
0 |
0 |
0 |
17 |
1 |
2 |
5 |
65 |

Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models |
0 |
0 |
1 |
25 |
0 |
1 |
8 |
20 |

Plug-in regularized estimation of high dimensional parameters in nonlinear semiparametric models |
0 |
0 |
0 |
0 |
0 |
0 |
6 |
15 |

Post-Selection Inference for Generalized Linear Models with Many Controls |
0 |
1 |
7 |
9 |
0 |
2 |
10 |
23 |

Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments |
0 |
0 |
1 |
2 |
3 |
3 |
5 |
20 |

Post-l1-penalized estimators in high-dimensional linear regression models |
1 |
1 |
3 |
47 |
2 |
2 |
8 |
154 |

Post-selection and post-regularization inference in linear models with many controls and instruments |
0 |
0 |
1 |
38 |
1 |
1 |
13 |
126 |

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

Posterior inference in curved exponential families under increasing dimensions |
0 |
0 |
0 |
2 |
0 |
0 |
3 |
22 |

Practical and robust $t$-test based inference for synthetic control and related methods |
1 |
2 |
4 |
33 |
4 |
8 |
25 |
42 |

Program Evaluation and Causal Inference with High-Dimensional Data |
0 |
0 |
0 |
4 |
1 |
3 |
15 |
30 |

Program evaluation and causal inference with high-dimensional data |
0 |
1 |
6 |
21 |
3 |
7 |
16 |
92 |

Program evaluation with high-dimensional data |
0 |
0 |
1 |
8 |
2 |
4 |
11 |
53 |

Program evaluation with high-dimensional data |
0 |
0 |
2 |
13 |
1 |
5 |
17 |
76 |

Program evaluation with high-dimensional data |
0 |
0 |
0 |
4 |
1 |
4 |
11 |
38 |

Program evaluation with high-dimensional data |
0 |
1 |
4 |
69 |
1 |
5 |
19 |
146 |

QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING |
0 |
0 |
0 |
71 |
1 |
7 |
13 |
317 |

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
0 |
0 |
1 |
0 |
2 |
11 |
20 |

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management |
0 |
0 |
1 |
43 |
0 |
4 |
18 |
71 |

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk |
1 |
1 |
3 |
17 |
3 |
5 |
18 |
32 |

Quantile Models with Endogeneity |
0 |
0 |
0 |
1 |
0 |
1 |
8 |
17 |

Quantile Regression under Misspecification |
0 |
0 |
0 |
2 |
1 |
5 |
21 |
411 |

Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
0 |
1 |
3 |
280 |
3 |
10 |
25 |
856 |

Quantile Regression with Censoring and Endogeneity |
0 |
0 |
1 |
1 |
0 |
6 |
32 |
38 |

Quantile Regression with Censoring and Endogeneity |
0 |
0 |
0 |
109 |
2 |
8 |
26 |
305 |

Quantile Regression with Censoring and Endogeneity |
0 |
0 |
0 |
49 |
0 |
5 |
20 |
133 |

Quantile and Average Effects in Nonseparable Panel Models |
0 |
0 |
0 |
25 |
0 |
2 |
5 |
95 |

Quantile and Probability Curves Without Crossing |
0 |
1 |
1 |
2 |
3 |
7 |
13 |
20 |

Quantile and Probability Curves without Crossing |
1 |
1 |
1 |
17 |
3 |
7 |
21 |
101 |

Quantile and Probability Curves without Crossing |
0 |
0 |
0 |
1 |
2 |
7 |
18 |
38 |

Quantile and average effects in nonseparable panel models |
0 |
0 |
0 |
43 |
0 |
2 |
6 |
110 |

Quantile and probability curves without crossing |
0 |
1 |
1 |
66 |
3 |
9 |
14 |
259 |

Quantile graphical models: prediction and conditional independence with applications to systemic risk |
0 |
0 |
0 |
32 |
0 |
4 |
15 |
27 |

Quantile models with endogeneity |
0 |
0 |
0 |
86 |
3 |
4 |
45 |
180 |

Quantile regression with censoring and endogeneity |
0 |
0 |
0 |
40 |
1 |
5 |
22 |
130 |

Quantreg.nonpar: an R package for performing nonparametric series quantile regression |
1 |
1 |
3 |
12 |
7 |
13 |
38 |
83 |

Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
0 |
0 |
2 |
5 |
6 |

Rearranging Edgeworth-Cornish-Fisher expansions |
0 |
0 |
1 |
89 |
0 |
3 |
9 |
326 |

Robust inference in high-dimensional approximately sparse quantile regression models |
0 |
1 |
2 |
13 |
2 |
6 |
15 |
72 |

Semi-Parametric Efficient Policy Learning with Continuous Actions |
0 |
0 |
3 |
3 |
0 |
1 |
12 |
13 |

Semi-Parametric Efficient Policy Learning with Continuous Actions |
0 |
1 |
6 |
6 |
0 |
1 |
7 |
7 |

Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models |
0 |
0 |
3 |
20 |
3 |
10 |
32 |
83 |

Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models |
0 |
0 |
0 |
28 |
4 |
9 |
19 |
50 |

Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
2 |
1 |
6 |
13 |
29 |

Set identification with Tobin regressors |
0 |
0 |
0 |
63 |
2 |
4 |
11 |
171 |

Shape-Enforcing Operators for Point and Interval Estimators |
0 |
0 |
1 |
24 |
3 |
5 |
21 |
43 |

Simultaneous Confidence Intervals for High-dimensional Linear Models with Many Endogenous Variables |
0 |
0 |
1 |
27 |
1 |
1 |
6 |
16 |

Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables |
0 |
0 |
1 |
3 |
2 |
2 |
6 |
10 |

Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions |
0 |
4 |
14 |
45 |
2 |
13 |
42 |
64 |

Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models |
0 |
0 |
0 |
15 |
0 |
0 |
3 |
29 |

Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models |
0 |
0 |
0 |
2 |
0 |
0 |
5 |
20 |

Single market non-parametric identification of multi-attribute hedonic equilibrium models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |

Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results |
0 |
1 |
2 |
4 |
0 |
6 |
16 |
24 |

SortedEffects: Sorted Causal Effects in R |
1 |
1 |
2 |
2 |
1 |
4 |
14 |
14 |

Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain |
0 |
0 |
6 |
10 |
0 |
3 |
19 |
36 |

Sparse models and methods for optimal instruments with an application to eminent domain |
0 |
0 |
0 |
42 |
1 |
4 |
17 |
137 |

Subvector Inference in Partially Identified Models with Many Moment Inequalities |
0 |
0 |
0 |
20 |
0 |
1 |
5 |
23 |

Subvector inference in PI models with many moment inequalities |
0 |
0 |
19 |
19 |
0 |
1 |
4 |
4 |

Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls" |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
6 |

Testing Many Moment Inequalities |
0 |
0 |
3 |
12 |
1 |
1 |
8 |
72 |

Testing many moment inequalities |
0 |
0 |
1 |
14 |
1 |
2 |
8 |
33 |

Testing many moment inequalities |
0 |
1 |
4 |
21 |
3 |
4 |
12 |
32 |

The Impact of Big Data on Firm Performance: An Empirical Investigation |
3 |
8 |
31 |
111 |
7 |
21 |
69 |
173 |

The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
0 |
1 |
7 |
3 |
7 |
20 |
29 |

The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
3 |
13 |
1 |
4 |
20 |
65 |

Uniform Inference in High-Dimensional Gaussian Graphical Models |
1 |
1 |
3 |
30 |
2 |
4 |
15 |
33 |

Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems |
0 |
0 |
0 |
1 |
3 |
3 |
13 |
17 |

Uniform inference in high-dimensional Gaussian graphical models |
0 |
0 |
11 |
11 |
0 |
1 |
6 |
6 |

Uniform post selection inference for LAD regression and other Z-estimation problems |
0 |
0 |
0 |
18 |
3 |
4 |
10 |
34 |

Uniform post selection inference for LAD regression and other z-estimation problems |
0 |
0 |
1 |
3 |
2 |
2 |
12 |
64 |

Uniform post selection inference for LAD regression models |
0 |
1 |
1 |
31 |
1 |
2 |
11 |
87 |

Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models |
0 |
0 |
0 |
0 |
3 |
6 |
13 |
23 |

Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach |
0 |
0 |
0 |
1 |
1 |
1 |
7 |
10 |

Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R) |
0 |
0 |
4 |
21 |
2 |
3 |
12 |
32 |

Valid post-selection and post-regularization inference: An elementary, general approach |
2 |
3 |
5 |
21 |
3 |
4 |
18 |
34 |

Valid post-selection inference in high-dimensional approximately sparse quantile regression models |
0 |
0 |
1 |
15 |
2 |
6 |
16 |
43 |

Valid simultaneous inference in high-dimensional settings (with the HDM package for R) |
0 |
0 |
2 |
2 |
0 |
0 |
4 |
4 |

Vector Quantile Regression |
1 |
1 |
3 |
54 |
1 |
2 |
18 |
88 |

Vector Quantile Regression |
0 |
0 |
1 |
3 |
2 |
3 |
17 |
38 |

Vector Quantile Regression: An Optimal Transport Approach |
0 |
1 |
3 |
22 |
0 |
1 |
16 |
60 |

Vector quantile regression |
0 |
0 |
0 |
9 |
1 |
3 |
14 |
36 |

Vector quantile regression: an optimal transport approach |
0 |
0 |
1 |
20 |
1 |
3 |
9 |
38 |

hdm: High-Dimensional Metrics |
0 |
0 |
0 |
2 |
0 |
0 |
3 |
18 |

hdm: High-Dimensional Metrics |
0 |
0 |
0 |
6 |
1 |
1 |
4 |
22 |

quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression |
0 |
0 |
1 |
4 |
6 |
10 |
16 |
32 |

Total Working Papers |
58 |
168 |
787 |
7,226 |
437 |
1,277 |
4,116 |
20,462 |