| 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" |
2 |
20 |
56 |
56 |
16 |
95 |
201 |
201 |
| A Simple, Graphical Approach to Comparing Multiple Treatments |
1 |
1 |
1 |
121 |
4 |
6 |
13 |
209 |
| A simple, graphical approach to comparing multiple treatment |
0 |
0 |
0 |
6 |
2 |
3 |
5 |
24 |
| An Experimental Test of the No Safety Schools Theorem |
0 |
0 |
0 |
51 |
6 |
11 |
13 |
150 |
| Bootstrap And Asymptotic Inference With Multiway Clustering |
0 |
1 |
3 |
240 |
8 |
11 |
27 |
492 |
| Bootstrap and Asymptotic Inference with Multiway Clustering |
0 |
0 |
0 |
0 |
4 |
4 |
4 |
7 |
| Cluster-Robust Inference: A Guide to Empirical Practice |
2 |
5 |
22 |
447 |
7 |
23 |
76 |
895 |
| Cluster-Robust Inference: A Guide to Empirical Practice |
0 |
0 |
0 |
13 |
5 |
9 |
18 |
47 |
| Cluster-Robust Jackknife and Bootstrap Inference for Binary Response Models |
0 |
0 |
2 |
5 |
2 |
7 |
14 |
22 |
| Cluster-robust jackknife and bootstrap inference for logistic regression models |
4 |
8 |
18 |
32 |
16 |
36 |
63 |
92 |
| Cluster–robust inference: A guide to empirical practice |
0 |
0 |
1 |
21 |
10 |
20 |
33 |
107 |
| Comparing Human-Only, AI-Assisted, and AI-Led Teams on Assessing Research Reproducibility in Quantitative Social Science |
6 |
14 |
115 |
194 |
41 |
139 |
685 |
922 |
| Decision Making with Risky, Rival Outcomes: Theory and Evidence |
0 |
0 |
0 |
181 |
3 |
6 |
11 |
197 |
| Difference in differences with unpoolable data |
0 |
0 |
3 |
19 |
5 |
6 |
20 |
51 |
| Difference-in-Differences with Unpoolable Data |
0 |
0 |
2 |
18 |
2 |
5 |
15 |
36 |
| Fast And Wild: Bootstrap Inference In Stata Using Boottest |
9 |
16 |
57 |
1,081 |
41 |
100 |
288 |
3,426 |
| Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference |
0 |
0 |
0 |
5 |
2 |
5 |
8 |
32 |
| Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference |
0 |
2 |
5 |
137 |
4 |
12 |
23 |
269 |
| Fast and Wild: Bootstrap Inference in Stata Using boottest |
0 |
1 |
2 |
47 |
6 |
9 |
21 |
225 |
| Finish It and It’s Free: An Evaluation of College Graduation Subsidies |
0 |
0 |
0 |
42 |
2 |
3 |
7 |
85 |
| Good Controls Gone Bad: Difference-in-Differences with Covariates |
0 |
2 |
15 |
28 |
3 |
14 |
24 |
32 |
| How Targeted Is Targeted Tax Relief? Evidence From The Unemployment Insurance Youth Hires Program |
0 |
0 |
0 |
85 |
6 |
10 |
15 |
381 |
| How Targeted is Targeted Tax Relief? Evidence from the Unemployment Insurance Youth Hires Program |
0 |
0 |
0 |
0 |
15 |
16 |
16 |
17 |
| Immigrant Category of Admission and the Earnings of Adults and Children: How far does the Apple Fall? |
0 |
0 |
0 |
45 |
2 |
5 |
11 |
139 |
| Immigrant Category of Admission of the Parents and Outcomes of the Children: How far does the Apple Fall? |
0 |
0 |
1 |
85 |
0 |
1 |
5 |
193 |
| Jackknife Inference with Two-Way Clustering |
0 |
0 |
1 |
8 |
1 |
4 |
8 |
30 |
| Jackknife inference for multiway clustering and CS-DiD in Stata: twowayjack and csdidjack |
0 |
3 |
10 |
10 |
9 |
16 |
29 |
29 |
| Jackknife inference with two-way clustering |
1 |
6 |
24 |
41 |
5 |
14 |
50 |
85 |
| Jackknife methods for improved cluster–robust inference |
0 |
1 |
2 |
13 |
3 |
6 |
10 |
26 |
| Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust |
0 |
0 |
2 |
74 |
4 |
9 |
17 |
154 |
| Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust |
0 |
0 |
0 |
20 |
2 |
3 |
8 |
54 |
| Memory-safe massive Monte Carlo: A practical guide |
0 |
1 |
4 |
4 |
8 |
15 |
19 |
19 |
| One Sided Matching: Choice Selection With Rival Uncertain Outcomes |
0 |
0 |
0 |
71 |
1 |
8 |
8 |
106 |
| One Sided Matching: Choice Selection With Rival Uncertain Outcomes |
0 |
0 |
0 |
0 |
2 |
3 |
4 |
50 |
| Pitfalls When Estimating Treatment Effects Using Clustered Data |
0 |
1 |
4 |
137 |
16 |
22 |
32 |
315 |
| Pitfalls when Estimating Treatment Effects Using Clustered Data |
0 |
0 |
0 |
0 |
9 |
10 |
10 |
10 |
| Randomization Inference For Difference-in-differences With Few Treated Clusters |
2 |
2 |
16 |
868 |
20 |
60 |
287 |
6,126 |
| Randomization Inference for Difference-in-Differences with Few Treated Clusters |
0 |
0 |
0 |
2 |
3 |
3 |
3 |
6 |
| Randomization Inference for Difference-in-Differences with Few Treated Clusters |
0 |
0 |
1 |
38 |
2 |
5 |
19 |
152 |
| Reworking Wild Bootstrap Based Inference For Clustered Errors |
2 |
8 |
19 |
718 |
20 |
48 |
103 |
1,921 |
| Reworking Wild Bootstrap Based Inference for Clustered Errors |
0 |
0 |
0 |
0 |
6 |
17 |
17 |
18 |
| Targeting Tax Relief at Youth Employment |
0 |
0 |
0 |
57 |
3 |
5 |
10 |
131 |
| Testing for the appropriate level of clustering in linear regression models |
0 |
1 |
4 |
386 |
1 |
8 |
20 |
838 |
| Testing for the appropriate level of clustering in linear regression models |
0 |
0 |
0 |
19 |
6 |
8 |
13 |
47 |
| The Many Misspellings of Albuquerque: A Comment on 'Sorting or Steering: The Effects of Housing Discrimination on Neighborhood Choice' |
0 |
8 |
14 |
77 |
3 |
43 |
73 |
303 |
| The Subcluster Wild Bootstrap for Few (Treated) Clusters |
0 |
0 |
1 |
17 |
3 |
7 |
12 |
77 |
| The Wild Bootstrap For Few (treated) Clusters |
0 |
0 |
1 |
155 |
4 |
14 |
22 |
319 |
| The Wild Bootstrap for Few (Treated) Clusters |
0 |
0 |
0 |
1 |
4 |
7 |
7 |
11 |
| The multiway cluster wild bootstrap |
0 |
0 |
2 |
65 |
3 |
6 |
11 |
126 |
| Using Images as Covariates: Measuring Curb Appeal with Deep Learning |
0 |
0 |
0 |
6 |
6 |
9 |
11 |
24 |
| When and How to Deal with Clustered Errors in Regression Models |
1 |
3 |
11 |
219 |
8 |
18 |
47 |
468 |
| Wild Bootstrap Inference For Wildly Different Cluster Sizes |
0 |
0 |
0 |
400 |
6 |
9 |
23 |
986 |
| Wild Bootstrap Inference for Wildly Different Cluster Sizes |
0 |
0 |
0 |
0 |
5 |
8 |
8 |
9 |
| Wild Bootstrap Randomization Inference For Few Treated Clusters |
0 |
0 |
3 |
200 |
3 |
8 |
17 |
368 |
| Wild Bootstrap Randomization Inference for Few Treated Clusters |
0 |
0 |
0 |
1 |
5 |
5 |
5 |
8 |
| Wild Bootstrap and Asymptotic Inference with Multiway Clustering |
0 |
1 |
5 |
167 |
6 |
14 |
25 |
372 |
| Wild Bootstrap and Asymptotic Inference with Multiway Clustering |
0 |
0 |
1 |
21 |
4 |
6 |
10 |
64 |
| undid: A Stata package for difference in differences with unpoolable data |
0 |
1 |
4 |
4 |
3 |
6 |
10 |
10 |
| Total Working Papers |
30 |
106 |
432 |
6,758 |
396 |
980 |
2,564 |
21,513 |
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