Working Paper |
File Downloads |
Abstract Views |
Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
7 |
0 |
0 |
3 |
47 |
A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
15 |
A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
8 |
A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
A lava attack on the recovery of sums of dense and sparse signals |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
37 |
Double machine learning for treatment and causal parameters |
0 |
0 |
3 |
118 |
0 |
1 |
19 |
532 |
Double machine learning for treatment and causal parameters |
0 |
0 |
1 |
5 |
2 |
4 |
13 |
32 |
Double/Debiased Machine Learning for Treatment and Causal Parameters |
9 |
14 |
181 |
1,078 |
29 |
57 |
494 |
2,810 |
Double/Debiased Machine Learning for Treatment and Structural Parameters |
1 |
1 |
4 |
120 |
4 |
13 |
79 |
435 |
Double/debiased machine learning for treatment and structural parameters |
0 |
1 |
4 |
38 |
4 |
7 |
31 |
129 |
Double/debiased machine learning for treatment and structural parameters |
0 |
2 |
5 |
6 |
1 |
4 |
17 |
27 |
Estimation of treatment effects with high-dimensional controls |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
Estimation of treatment effects with high-dimensional controls |
0 |
0 |
0 |
38 |
0 |
0 |
2 |
75 |
Estimation with many instrumental variables |
0 |
0 |
3 |
174 |
0 |
2 |
9 |
453 |
Finite-Sample Inference Methods for Quantile Regression Models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
250 |
High dimensional methods and inference on structural and treatment effects |
0 |
0 |
0 |
1 |
0 |
1 |
5 |
11 |
High dimensional methods and inference on structural and treatment effects |
0 |
0 |
0 |
22 |
1 |
1 |
5 |
114 |
High-Dimensional Econometrics and Regularized GMM |
0 |
0 |
1 |
59 |
0 |
2 |
20 |
176 |
High-dimensional econometrics and regularized GMM |
0 |
0 |
0 |
13 |
1 |
1 |
6 |
86 |
Inference for High-Dimensional Sparse Econometric Models |
0 |
1 |
2 |
14 |
0 |
5 |
13 |
87 |
Inference for Low-Rank Models |
1 |
1 |
4 |
48 |
2 |
6 |
10 |
72 |
Inference for heterogeneous effects using low-rank estimations |
1 |
1 |
1 |
18 |
2 |
5 |
11 |
57 |
Inference for high-dimensional sparse econometric models |
0 |
0 |
0 |
56 |
1 |
1 |
3 |
188 |
Inference in High Dimensional Panel Models with an Application to Gun Control |
0 |
0 |
0 |
7 |
0 |
0 |
6 |
47 |
Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
25 |
0 |
1 |
4 |
88 |
Inference in high dimensional panel models with an application to gun control |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
6 |
Inference on Treatment Effects After Selection Amongst High-Dimensional Controls |
0 |
0 |
5 |
12 |
0 |
2 |
25 |
87 |
Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
0 |
1 |
3 |
4 |
6 |
Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
3 |
3 |
0 |
0 |
4 |
10 |
Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
0 |
14 |
1 |
1 |
1 |
102 |
Inference on treatment effects after selection amongst high-dimensional controls |
0 |
0 |
1 |
46 |
0 |
0 |
4 |
138 |
Instrumental Variable Quantile Regression |
0 |
0 |
1 |
56 |
1 |
1 |
10 |
66 |
Instrumental variables estimation with flexible distribution |
0 |
0 |
0 |
39 |
0 |
2 |
2 |
163 |
LASSO Methods for Gaussian Instrumental Variables Models |
0 |
1 |
2 |
12 |
0 |
1 |
4 |
51 |
LASSOPACK and PDSLASSO: Prediction, model selection and causal inference with regularized regression |
0 |
2 |
7 |
175 |
0 |
4 |
29 |
551 |
Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments |
0 |
0 |
1 |
4 |
0 |
0 |
4 |
36 |
Post-selection and post-regularization inference in linear models with many controls and instruments |
0 |
0 |
0 |
40 |
0 |
0 |
4 |
152 |
Post-selection and post-regularization inference in linear models with many controls and instruments |
1 |
1 |
1 |
1 |
2 |
3 |
7 |
9 |
Pre-event Trends in the Panel Event-study Design |
2 |
4 |
4 |
53 |
3 |
8 |
15 |
269 |
Pre-event Trends in the Panel Event-study Design |
0 |
0 |
1 |
54 |
0 |
1 |
2 |
154 |
Program Evaluation and Causal Inference with High-Dimensional Data |
0 |
0 |
1 |
13 |
0 |
0 |
6 |
75 |
Program evaluation and causal inference with high-dimensional data |
0 |
0 |
0 |
27 |
0 |
0 |
2 |
121 |
Program evaluation and causal inference with high-dimensional data |
0 |
0 |
0 |
1 |
1 |
1 |
5 |
13 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
7 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
11 |
0 |
1 |
5 |
92 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
16 |
0 |
0 |
2 |
121 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
79 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
75 |
0 |
0 |
1 |
201 |
Program evaluation with high-dimensional data |
0 |
1 |
1 |
1 |
1 |
2 |
3 |
11 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
Quantile Models with Endogeneity |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
56 |
Quantile models with endogeneity |
0 |
0 |
0 |
90 |
0 |
0 |
1 |
242 |
Quantile models with endogeneity |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
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 |
1 |
1 |
1 |
0 |
2 |
2 |
3 |
Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
25 |
Some Flexible Parametric Models for Partially Adaptive Estimators of Econometric Models |
0 |
0 |
0 |
72 |
0 |
0 |
2 |
235 |
Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain |
1 |
1 |
2 |
20 |
1 |
3 |
10 |
83 |
Sparse models and methods for optimal instruments with an application to eminent domain |
0 |
0 |
0 |
43 |
2 |
2 |
6 |
162 |
Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls" |
0 |
0 |
0 |
2 |
0 |
1 |
2 |
23 |
Targeted undersmoothing |
0 |
0 |
0 |
25 |
0 |
0 |
3 |
62 |
The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications |
0 |
0 |
0 |
6 |
0 |
1 |
3 |
34 |
The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications |
0 |
0 |
1 |
47 |
0 |
0 |
3 |
85 |
The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications |
0 |
0 |
1 |
2 |
0 |
1 |
4 |
23 |
Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach |
0 |
0 |
1 |
4 |
0 |
1 |
2 |
26 |
Valid post-selection and post-regularization inference: An elementary, general approach |
0 |
0 |
0 |
22 |
0 |
0 |
1 |
45 |
Valid post-selection and post-regularization inference: An elementary, general approach |
0 |
0 |
0 |
0 |
1 |
1 |
6 |
7 |
Visualization, Identification, and Estimation in the Linear Panel Event Study Design |
1 |
1 |
8 |
60 |
4 |
7 |
35 |
228 |
Visualization, Identification, and Estimation in the Linear Panel Event-Study Design |
2 |
2 |
9 |
79 |
9 |
17 |
68 |
267 |
Visualization, Identification, and stimation in the Linear Panel Event-Study Design |
0 |
0 |
2 |
27 |
1 |
4 |
12 |
73 |
ddml: Double/Debiased Machine Learning in Stata |
0 |
0 |
1 |
24 |
0 |
1 |
6 |
35 |
ddml: Double/debiased machine learning in Stata |
0 |
1 |
3 |
34 |
0 |
2 |
12 |
58 |
ddml: Double/debiased machine learning in Stata |
0 |
0 |
3 |
32 |
2 |
3 |
19 |
82 |
hdm: High-Dimensional Metrics |
0 |
0 |
1 |
2 |
0 |
1 |
5 |
12 |
hdm: High-Dimensional Metrics |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
36 |
lassopack: Model Selection and Prediction with Regularized Regression in Stata |
0 |
0 |
3 |
38 |
0 |
0 |
6 |
169 |
lassopack: Model selection and prediction with regularized regression in Stata |
0 |
1 |
4 |
43 |
0 |
1 |
7 |
171 |
pystacked and ddml: machine learning for prediction and causal inference in Stata |
0 |
0 |
6 |
66 |
0 |
0 |
21 |
126 |
pystacked: Stacking generalization and machine learning in Stata |
0 |
1 |
1 |
14 |
0 |
2 |
8 |
45 |
pystacked: Stacking generalization and machine learning in Stata |
0 |
0 |
0 |
17 |
0 |
2 |
8 |
38 |
xtevent: Estimation and visualization in the linear panel event-study design |
4 |
11 |
11 |
11 |
7 |
29 |
29 |
29 |
Total Working Papers |
23 |
49 |
295 |
3,334 |
84 |
222 |
1,200 |
10,812 |