| Working Paper |
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Abstract Views |
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3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Neyman-Orthogonalization Approach to The Incidental Parameter Problem |
0 |
0 |
0 |
2 |
1 |
4 |
21 |
28 |
| A Neyman-Orthogonalization Approach to the Incidental Parameter Problem |
0 |
0 |
0 |
11 |
0 |
4 |
12 |
39 |
| A neyman-orthogonalization approach to the incidental parameter problem |
0 |
1 |
1 |
1 |
1 |
4 |
16 |
17 |
| A neyman-orthogonalization approach to the incidental parameter problem |
0 |
0 |
0 |
0 |
1 |
7 |
22 |
22 |
| Analysis of interactive fixed effects dynamic linear panel regression with measurement error |
1 |
7 |
7 |
7 |
0 |
0 |
0 |
0 |
| Analysis of interactive fixed effects dynamic linear panel regression with measurement error |
0 |
0 |
0 |
72 |
0 |
2 |
9 |
247 |
| Approximate Functional Differencing |
0 |
0 |
0 |
26 |
0 |
1 |
6 |
15 |
| Approximate Operator Inversion for Average Effects in Nonlinear Panel Models |
0 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
| Bias and Consistency in Three-way Gravity Models |
0 |
0 |
0 |
59 |
0 |
5 |
17 |
144 |
| Bias and Consistency in Three-way Gravity Models |
0 |
0 |
0 |
8 |
0 |
1 |
6 |
15 |
| Bias and consistency in three-way gravity models |
0 |
0 |
0 |
9 |
1 |
5 |
12 |
24 |
| Binary choice logit models with general fixed effects for panel and network data |
0 |
0 |
13 |
13 |
0 |
2 |
14 |
14 |
| Bounding Treatment Effects by Pooling Limited Information across Observations |
0 |
0 |
1 |
27 |
0 |
1 |
7 |
20 |
| Bounds On Treatment Effects On Transitions |
0 |
0 |
0 |
0 |
0 |
1 |
9 |
11 |
| Bounds On Treatment Effects On Transitions |
0 |
0 |
0 |
33 |
0 |
2 |
6 |
36 |
| Bounds On Treatment Effects On Transitions |
0 |
0 |
0 |
13 |
0 |
0 |
4 |
34 |
| Bounds on Average Effects in Discrete Choice Panel Data Models |
0 |
1 |
1 |
13 |
1 |
6 |
14 |
29 |
| Bounds on treatment effects on transitions |
0 |
0 |
0 |
3 |
0 |
3 |
10 |
38 |
| Bounds on treatment effects on transitions |
0 |
0 |
0 |
0 |
0 |
3 |
9 |
10 |
| Dynamic Linear Panel Regression Models with Interactive Fixed Effects |
0 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
| Dynamic Ordered Panel Logit Models |
0 |
0 |
0 |
18 |
0 |
7 |
18 |
56 |
| Dynamic Ordered Panel Logit Models |
0 |
0 |
0 |
24 |
1 |
10 |
22 |
77 |
| Dynamic linear panel regression models with interactive fixed effects |
0 |
0 |
2 |
3 |
0 |
2 |
12 |
21 |
| Dynamic linear panel regression models with interactive fixed effects |
0 |
0 |
0 |
0 |
0 |
1 |
7 |
9 |
| Dynamic linear panel regression models with interactive fixed effects |
0 |
0 |
0 |
59 |
0 |
4 |
15 |
192 |
| Dynamic linear panel regression models with interactive fixed effects |
0 |
0 |
0 |
36 |
0 |
5 |
14 |
96 |
| Estimation of random coefficients logit demand models with interactive fixed effects |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
| Estimation of random coefficients logit demand models with interactive fixed effects |
0 |
0 |
0 |
0 |
1 |
4 |
12 |
14 |
| Estimation of random coefficients logit demand models with interactive fixed effects |
0 |
0 |
0 |
0 |
0 |
1 |
6 |
9 |
| Estimation of random coefficients logit demand models with interactive fixed effects |
0 |
0 |
0 |
20 |
0 |
0 |
7 |
50 |
| Estimation of random coefficients logit demand models with interactive fixed effects |
0 |
0 |
0 |
42 |
1 |
2 |
11 |
98 |
| Estimation of random coefficients logit demand models with interactive fixed effects |
0 |
11 |
11 |
11 |
0 |
1 |
1 |
1 |
| Estimation of random coefficients logit demand models with interactive fixed effects |
0 |
0 |
0 |
105 |
2 |
5 |
13 |
256 |
| Factor-Augmented Panel Regressions and Variance-Weighted Treatment Effects |
1 |
15 |
15 |
15 |
2 |
17 |
17 |
17 |
| Fixed Effect Estimation of Large T Panel Data Models |
1 |
1 |
3 |
93 |
1 |
4 |
16 |
124 |
| Fixed effect estimation of large T panel data models |
0 |
0 |
2 |
3 |
0 |
1 |
14 |
17 |
| Fixed effect estimation of large T panel data models |
0 |
0 |
0 |
9 |
1 |
4 |
12 |
49 |
| Fixed effect estimation of large T panel data models |
0 |
0 |
2 |
26 |
0 |
0 |
16 |
144 |
| Fixed-Effect Regressions on Network Data |
0 |
0 |
0 |
43 |
0 |
2 |
17 |
122 |
| Fixed-Effect Regressions on Network Data |
0 |
0 |
1 |
61 |
0 |
4 |
21 |
184 |
| Fixed-effect regressions on network data |
0 |
0 |
0 |
6 |
0 |
2 |
8 |
25 |
| Fixed-effect regressions on network data |
0 |
0 |
0 |
27 |
1 |
2 |
8 |
52 |
| Fixed-effect regressions on network data |
0 |
0 |
0 |
19 |
1 |
4 |
12 |
65 |
| Fixed-effect regressions on network data |
0 |
0 |
0 |
12 |
0 |
1 |
10 |
48 |
| Fixed-effect regressions on network data |
0 |
0 |
0 |
1 |
1 |
2 |
20 |
26 |
| Fixed-effect regressions on network data |
0 |
0 |
0 |
0 |
1 |
4 |
17 |
20 |
| Forecasted Treatment Effects |
0 |
0 |
0 |
6 |
0 |
2 |
10 |
30 |
| Forecasted Treatment Effects |
0 |
0 |
0 |
24 |
0 |
1 |
5 |
25 |
| Higher-Order Neyman Orthogonality in Moment-Condition Models |
5 |
5 |
5 |
5 |
3 |
4 |
4 |
4 |
| Individual and Time Effects in Nonlinear Panel Models with Large N, T |
0 |
0 |
1 |
34 |
0 |
3 |
13 |
185 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
20 |
0 |
6 |
13 |
105 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
45 |
1 |
4 |
14 |
119 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
7 |
0 |
3 |
18 |
115 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
0 |
0 |
4 |
14 |
17 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
0 |
0 |
3 |
15 |
19 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
0 |
0 |
2 |
8 |
11 |
| Inference On A Distribution From Noisy Draws |
0 |
0 |
0 |
4 |
1 |
3 |
7 |
15 |
| Inference on a Distribution from Noisy Draws |
0 |
0 |
0 |
26 |
0 |
2 |
8 |
54 |
| Inference on a distribution from noisy draws |
0 |
0 |
0 |
1 |
0 |
1 |
6 |
23 |
| Inference on a distribution from noisy draws |
0 |
0 |
0 |
0 |
0 |
1 |
8 |
10 |
| Inference on a distribution from noisy draws |
0 |
0 |
0 |
2 |
0 |
4 |
9 |
32 |
| Inference on a distribution from noisy draws |
0 |
0 |
0 |
6 |
0 |
1 |
15 |
44 |
| Inference on a distribution from noisy draws |
0 |
0 |
0 |
4 |
1 |
6 |
13 |
44 |
| Linear Panel Regressions with Two-Way Unobserved Heterogeneity |
0 |
0 |
0 |
22 |
0 |
4 |
12 |
40 |
| Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
| Linear panel regressions with two-way unobserved heterogeneity |
0 |
0 |
0 |
3 |
0 |
4 |
14 |
24 |
| Linear regression for panel with unknown number of factors as interactive fixed effects |
0 |
0 |
0 |
0 |
0 |
3 |
8 |
11 |
| Linear regression for panel with unknown number of factors as interactive fixed effects |
0 |
0 |
0 |
11 |
0 |
2 |
11 |
60 |
| Linear regression for panel with unknown number of factors as interactive fixed effects |
0 |
0 |
0 |
1 |
0 |
1 |
6 |
10 |
| Linear regression for panel with unknown number of factors as interactive fixed effects |
0 |
0 |
0 |
77 |
3 |
5 |
25 |
207 |
| Low-Rank Approximations of Nonseparable Panel Models |
0 |
0 |
0 |
19 |
1 |
3 |
11 |
50 |
| Low-rank approximations of nonseparable panel models |
0 |
0 |
0 |
1 |
0 |
4 |
11 |
26 |
| Low-rank approximations of nonseparable panel models |
0 |
0 |
0 |
4 |
1 |
3 |
15 |
27 |
| Minimizing Sensitivity to Model Misspecification |
0 |
0 |
0 |
43 |
1 |
3 |
12 |
99 |
| Minimizing Sensitivity to Model Misspecification |
0 |
0 |
0 |
3 |
0 |
2 |
16 |
28 |
| Minimizing sensitivity to model misspecification |
0 |
0 |
0 |
5 |
0 |
0 |
5 |
44 |
| Moment Conditions for Dynamic Panel Logit Models with Fixed Effects |
0 |
0 |
0 |
13 |
0 |
6 |
15 |
54 |
| Moment Conditions for Dynamic Panel Logit Models with Fixed Effects |
0 |
0 |
1 |
31 |
2 |
3 |
10 |
57 |
| Moment Conditions for Dynamic Panel Logit Models with Fixed Effects |
0 |
0 |
0 |
5 |
1 |
5 |
20 |
38 |
| Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
0 |
5 |
0 |
3 |
12 |
24 |
| Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
0 |
50 |
3 |
7 |
15 |
113 |
| Network and panel quantile effects via distribution regression |
0 |
0 |
0 |
2 |
0 |
1 |
8 |
31 |
| Network and panel quantile effects via distribution regression |
0 |
0 |
0 |
11 |
0 |
1 |
11 |
41 |
| Nonlinear Factor Models for Network and Panel Data |
0 |
0 |
0 |
12 |
0 |
5 |
17 |
74 |
| Nonlinear factor models for network and panel data |
0 |
0 |
0 |
28 |
0 |
3 |
20 |
81 |
| Nonlinear factor models for network and panel data |
0 |
0 |
0 |
5 |
0 |
0 |
7 |
39 |
| Nuclear Norm Regularized Estimation of Panel Regression Models |
0 |
1 |
2 |
32 |
1 |
3 |
26 |
90 |
| Nuclear norm regularized estimation of panel regression models |
0 |
0 |
1 |
10 |
1 |
6 |
28 |
82 |
| Posterior Average Effects |
0 |
0 |
0 |
27 |
0 |
4 |
12 |
76 |
| Posterior average effects |
0 |
0 |
0 |
4 |
0 |
1 |
6 |
7 |
| Posterior average effects |
0 |
0 |
0 |
1 |
0 |
2 |
12 |
19 |
| Posterior average effects |
0 |
0 |
0 |
1 |
1 |
3 |
7 |
25 |
| Robust Estimation and Inference in Panels with Interactive Fixed Effects |
0 |
0 |
0 |
27 |
1 |
6 |
12 |
28 |
| Robust estimation and inference in panels with interactive fixed effects |
0 |
0 |
0 |
0 |
0 |
3 |
13 |
15 |
| Robust estimation and inference in panels with interactive fixed effects |
0 |
0 |
1 |
3 |
0 |
7 |
29 |
33 |
| Simultaneity in Binary Outcome Models with an Application to Employment for Couples |
0 |
0 |
0 |
4 |
0 |
6 |
30 |
36 |
| Simultaneity in Binary Outcome Models with an Application to Employment for Couples |
0 |
0 |
1 |
31 |
0 |
5 |
15 |
52 |
| probitfe and logitfe: Bias corrections for probit and logit models with two-way fixed effects |
0 |
1 |
1 |
47 |
0 |
5 |
21 |
137 |
| Total Working Papers |
8 |
45 |
74 |
1,654 |
40 |
312 |
1,198 |
5,153 |