| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Comment on "Delivering Remote Learning Using a Low-Tech Solution: Evidence from a Randomized Controlled Trial in Bangladesh" |
0 |
2 |
64 |
64 |
2 |
20 |
234 |
234 |
| A Simple, Graphical Approach to Comparing Multiple Treatments |
0 |
0 |
1 |
121 |
0 |
4 |
13 |
214 |
| A simple, graphical approach to comparing multiple treatment |
0 |
0 |
0 |
6 |
0 |
0 |
5 |
26 |
| An Experimental Test of the No Safety Schools Theorem |
0 |
0 |
0 |
51 |
0 |
0 |
13 |
151 |
| Bootstrap And Asymptotic Inference With Multiway Clustering |
0 |
0 |
2 |
240 |
0 |
8 |
31 |
504 |
| Bootstrap and Asymptotic Inference with Multiway Clustering |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
7 |
| Cluster-Robust Inference: A Guide to Empirical Practice |
0 |
3 |
19 |
454 |
3 |
16 |
67 |
919 |
| Cluster-Robust Inference: A Guide to Empirical Practice |
0 |
0 |
0 |
13 |
0 |
2 |
19 |
50 |
| Cluster-Robust Jackknife and Bootstrap Inference for Binary Response Models |
0 |
0 |
1 |
5 |
0 |
4 |
15 |
26 |
| Cluster-robust jackknife and bootstrap inference for logistic regression models |
0 |
0 |
14 |
34 |
0 |
4 |
57 |
100 |
| Cluster–robust inference: A guide to empirical practice |
1 |
1 |
1 |
22 |
4 |
8 |
58 |
139 |
| Comparing Human-Only, AI-Assisted, and AI-Led Teams on Assessing Research Reproducibility in Quantitative Social Science |
10 |
31 |
78 |
234 |
40 |
208 |
606 |
1,232 |
| Computational Reproducibility and Robustness of Empirical Economics and Political Science Research Between 2022 and 2023 |
3 |
231 |
231 |
231 |
23 |
753 |
753 |
753 |
| Decision Making with Risky, Rival Outcomes: Theory and Evidence |
0 |
0 |
0 |
181 |
0 |
4 |
12 |
201 |
| Difference in differences with unpoolable data |
0 |
0 |
1 |
19 |
0 |
3 |
19 |
57 |
| Difference-in-Differences with Unpoolable Data |
0 |
0 |
1 |
18 |
0 |
0 |
12 |
37 |
| Fast And Wild: Bootstrap Inference In Stata Using Boottest |
3 |
9 |
60 |
1,103 |
32 |
93 |
369 |
3,598 |
| Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference |
0 |
1 |
6 |
140 |
1 |
10 |
34 |
289 |
| Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference |
0 |
0 |
0 |
5 |
0 |
4 |
13 |
38 |
| Fast and Wild: Bootstrap Inference in Stata Using boottest |
0 |
0 |
4 |
49 |
2 |
20 |
48 |
253 |
| Finish It and It’s Free: An Evaluation of College Graduation Subsidies |
0 |
0 |
0 |
42 |
0 |
4 |
10 |
90 |
| Good Controls Gone Bad: Difference-in-Differences with Covariates |
0 |
0 |
3 |
29 |
3 |
15 |
40 |
54 |
| How Targeted Is Targeted Tax Relief? Evidence From The Unemployment Insurance Youth Hires Program |
0 |
0 |
1 |
86 |
1 |
4 |
19 |
387 |
| How Targeted is Targeted Tax Relief? Evidence from the Unemployment Insurance Youth Hires Program |
0 |
0 |
0 |
0 |
1 |
4 |
20 |
21 |
| Immigrant Category of Admission and the Earnings of Adults and Children: How far does the Apple Fall? |
0 |
0 |
0 |
45 |
1 |
6 |
14 |
146 |
| Immigrant Category of Admission of the Parents and Outcomes of the Children: How far does the Apple Fall? |
0 |
0 |
0 |
85 |
1 |
2 |
5 |
197 |
| Improved Inference for CSDID Using the Cluster Jackknife |
0 |
10 |
20 |
20 |
6 |
32 |
60 |
60 |
| Jackknife Inference with Two-Way Clustering |
0 |
0 |
1 |
8 |
1 |
6 |
15 |
38 |
| Jackknife inference for multiway clustering and CS-DiD in Stata: twowayjack and csdidjack |
1 |
1 |
11 |
11 |
2 |
9 |
42 |
42 |
| Jackknife inference with two-way clustering |
1 |
1 |
16 |
43 |
2 |
4 |
34 |
92 |
| Jackknife methods for improved cluster–robust inference |
0 |
0 |
1 |
13 |
0 |
2 |
11 |
29 |
| Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust |
0 |
0 |
0 |
74 |
1 |
4 |
15 |
159 |
| Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust |
0 |
0 |
0 |
20 |
0 |
3 |
11 |
57 |
| Memory-safe massive Monte Carlo: A practical guide |
0 |
2 |
9 |
9 |
0 |
9 |
37 |
37 |
| One Sided Matching: Choice Selection With Rival Uncertain Outcomes |
0 |
0 |
0 |
0 |
0 |
1 |
5 |
52 |
| One Sided Matching: Choice Selection With Rival Uncertain Outcomes |
0 |
0 |
0 |
71 |
0 |
3 |
11 |
109 |
| Pitfalls When Estimating Treatment Effects Using Clustered Data |
0 |
0 |
2 |
137 |
3 |
6 |
35 |
323 |
| Pitfalls when Estimating Treatment Effects Using Clustered Data |
0 |
0 |
0 |
0 |
0 |
2 |
14 |
14 |
| Randomization Inference For Difference-in-differences With Few Treated Clusters |
0 |
2 |
9 |
871 |
5 |
20 |
177 |
6,175 |
| Randomization Inference for Difference-in-Differences with Few Treated Clusters |
0 |
1 |
1 |
3 |
0 |
7 |
11 |
14 |
| Randomization Inference for Difference-in-Differences with Few Treated Clusters |
0 |
1 |
1 |
39 |
1 |
6 |
21 |
160 |
| Reproducibility and robustness of economics and political science research |
0 |
7 |
7 |
7 |
1 |
26 |
26 |
26 |
| Reworking Wild Bootstrap Based Inference For Clustered Errors |
3 |
12 |
29 |
733 |
18 |
63 |
166 |
2,013 |
| Reworking Wild Bootstrap Based Inference for Clustered Errors |
0 |
0 |
0 |
0 |
0 |
4 |
24 |
25 |
| Targeting Tax Relief at Youth Employment |
0 |
0 |
0 |
57 |
1 |
5 |
10 |
136 |
| Testing for the appropriate level of clustering in linear regression models |
0 |
0 |
0 |
19 |
0 |
4 |
16 |
52 |
| Testing for the appropriate level of clustering in linear regression models |
0 |
0 |
1 |
386 |
2 |
10 |
26 |
850 |
| The Many Misspellings of Albuquerque: A Comment on 'Sorting or Steering: The Effects of Housing Discrimination on Neighborhood Choice' |
2 |
5 |
16 |
83 |
6 |
13 |
71 |
321 |
| The Subcluster Wild Bootstrap for Few (Treated) Clusters |
0 |
0 |
1 |
18 |
1 |
5 |
16 |
83 |
| The Wild Bootstrap For Few (treated) Clusters |
0 |
2 |
2 |
157 |
4 |
16 |
35 |
338 |
| The Wild Bootstrap for Few (Treated) Clusters |
0 |
0 |
0 |
1 |
0 |
2 |
10 |
14 |
| The multiway cluster wild bootstrap |
0 |
0 |
1 |
65 |
0 |
5 |
14 |
133 |
| Using Images as Covariates: Measuring Curb Appeal with Deep Learning |
0 |
0 |
0 |
6 |
0 |
0 |
11 |
25 |
| When and How to Deal with Clustered Errors in Regression Models |
0 |
3 |
11 |
224 |
7 |
21 |
61 |
497 |
| Wild Bootstrap Inference For Wildly Different Cluster Sizes |
0 |
0 |
3 |
403 |
4 |
14 |
38 |
1,010 |
| Wild Bootstrap Inference for Wildly Different Cluster Sizes |
0 |
0 |
0 |
0 |
0 |
2 |
10 |
11 |
| Wild Bootstrap Randomization Inference For Few Treated Clusters |
0 |
1 |
3 |
202 |
2 |
7 |
24 |
378 |
| Wild Bootstrap Randomization Inference for Few Treated Clusters |
0 |
0 |
1 |
2 |
1 |
3 |
10 |
13 |
| Wild Bootstrap and Asymptotic Inference with Multiway Clustering |
0 |
0 |
1 |
22 |
2 |
10 |
21 |
76 |
| Wild Bootstrap and Asymptotic Inference with Multiway Clustering |
0 |
0 |
3 |
167 |
3 |
5 |
26 |
377 |
| didint: A Stata-Julia tool for intersection difference in differences |
0 |
1 |
12 |
12 |
0 |
5 |
33 |
33 |
| undid: A Stata package for difference in differences with unpoolable data |
0 |
0 |
5 |
5 |
1 |
2 |
18 |
18 |
| Total Working Papers |
24 |
327 |
654 |
7,165 |
188 |
1,532 |
3,655 |
23,513 |