| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| Arellano-bond lasso estimator for dynamic linear panel models |
0 |
0 |
2 |
2 |
0 |
2 |
17 |
18 |
| Average and Quantile Effects in Nonseparable Panel Models |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
34 |
| Bias Correction in Panel Data Models with Individual Specific Parameters |
0 |
0 |
0 |
41 |
0 |
0 |
2 |
199 |
| Bias Corrections for Two-Step Fixed Effects Panel Data Estimators |
0 |
0 |
1 |
408 |
0 |
1 |
5 |
1,538 |
| Bias Corrections for Two-Step Fixed Effects Panel Data Estimators |
0 |
0 |
0 |
190 |
0 |
1 |
5 |
669 |
| Bias corrections for two-step fixed effects panel data estimators |
0 |
0 |
0 |
253 |
0 |
0 |
4 |
726 |
| Censored Quantile Instrumental Variable Estimation via Control Functions |
0 |
0 |
0 |
40 |
0 |
0 |
1 |
178 |
| Censored Quantile Instrumental Variable Estimation with Stata |
0 |
0 |
1 |
14 |
1 |
1 |
3 |
102 |
| Censored Quantile Instrumental Variable Estimation with Stata |
0 |
0 |
0 |
7 |
0 |
1 |
2 |
63 |
| Conditional Rank-Rank Regression |
0 |
0 |
1 |
1 |
4 |
4 |
6 |
6 |
| Conditional quantile processes based on series or many regressors |
0 |
0 |
1 |
4 |
0 |
0 |
3 |
8 |
| Conditional quantile processes based on series or many regressors |
0 |
0 |
1 |
49 |
1 |
1 |
3 |
114 |
| Conditional quantile processes based on series or many regressors |
0 |
0 |
0 |
15 |
1 |
1 |
3 |
51 |
| Counterfactual analysis in R: a vignette |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
13 |
| Counterfactual analysis in R: a vignette |
0 |
0 |
1 |
53 |
0 |
0 |
2 |
222 |
| Decomposing Changes in the Distribution of Real Hourly Wages in the U.S |
0 |
0 |
0 |
4 |
1 |
1 |
3 |
29 |
| Decomposing Real Wage Changes in the United States |
0 |
0 |
1 |
15 |
0 |
1 |
3 |
46 |
| Distribution Regression Difference-in-Differences |
0 |
1 |
2 |
2 |
1 |
2 |
9 |
9 |
| Distribution regression with sample selection and UK wage decomposition |
0 |
2 |
7 |
38 |
1 |
5 |
22 |
53 |
| Distribution regression with sample selection, with an application to wage decompositions in the UK |
0 |
0 |
0 |
2 |
1 |
1 |
4 |
40 |
| Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes |
0 |
0 |
0 |
7 |
1 |
1 |
1 |
11 |
| Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes |
0 |
0 |
1 |
16 |
0 |
0 |
1 |
17 |
| Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects |
0 |
0 |
0 |
141 |
0 |
2 |
3 |
588 |
| Evaluating the Role of Individual Specific Heterogeneity in the Relationship Between Subjective Health Assessments and Income |
0 |
0 |
0 |
46 |
0 |
0 |
1 |
122 |
| ExtrapoLATE-ing: External Validity and Overidentification in the LATE Framework |
1 |
1 |
4 |
172 |
1 |
1 |
10 |
548 |
| Extremal quantile regression: an overview |
0 |
0 |
2 |
2 |
0 |
0 |
4 |
5 |
| Extremal quantile regression: an overview |
0 |
0 |
2 |
7 |
0 |
1 |
4 |
42 |
| Fischer-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
0 |
0 |
0 |
1 |
2 |
5 |
13 |
113 |
| Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
1 |
1 |
21 |
251 |
5 |
22 |
101 |
730 |
| Fixed Effects Estimation of Structural Parameters and Marginal Effects in Panel Probit Models |
0 |
0 |
0 |
179 |
0 |
0 |
4 |
641 |
| Fixed effect estimation of large T panel data models |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
3 |
| Fixed effect estimation of large T panel data models |
0 |
0 |
0 |
9 |
1 |
1 |
2 |
37 |
| Fixed effect estimation of large T panel data models |
0 |
0 |
1 |
24 |
0 |
0 |
1 |
128 |
| Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
1 |
8 |
0 |
0 |
3 |
21 |
| Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
1 |
60 |
0 |
0 |
3 |
104 |
| Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India |
0 |
0 |
2 |
96 |
2 |
4 |
11 |
308 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
39 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
| Generic inference on quantile and quantile effect functions for discrete outcomes |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
51 |
| Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
0 |
4 |
63 |
0 |
3 |
11 |
122 |
| Generic machine learning inference on heterogenous treatment effects in randomized experiments |
0 |
1 |
1 |
3 |
1 |
5 |
16 |
37 |
| Hours Worked and the U.S. Distribution of Real Annual Earnings 1976–2016 |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
46 |
| IMPROVING ESTIMATES OF MONOTONE FUNCTIONS BY REARRANGEMENT |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
149 |
| INFERENCE ON COUNTERFACTUAL DISTRIBUTIONS |
0 |
0 |
0 |
108 |
0 |
0 |
4 |
388 |
| Identification and Estimation of Marginal Effects in Nonlinear Panel Models |
0 |
0 |
0 |
47 |
1 |
2 |
5 |
177 |
| Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
0 |
106 |
1 |
2 |
3 |
325 |
| Identification and estimation of marginal effects in nonlinear panel models |
0 |
0 |
1 |
31 |
0 |
0 |
2 |
117 |
| Improving Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
17 |
| Improving Point and Interval Estimates of Monotone Functions by Rearrangement |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
20 |
| Improving estimates of monotone functions by rearrangement |
0 |
0 |
0 |
58 |
0 |
0 |
1 |
227 |
| Improving point and interval estimates of monotone functions by rearrangement |
0 |
0 |
0 |
65 |
0 |
0 |
0 |
314 |
| Improving point and interval estimators of monotone functions by rearrangement |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Improving point and interval estimators of monotone functions by rearrangement |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Individual and Time Effects in Nonlinear Panel Models with Large N, T |
0 |
0 |
0 |
33 |
0 |
1 |
4 |
172 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
4 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
97 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
20 |
0 |
0 |
1 |
92 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
1 |
45 |
0 |
1 |
4 |
105 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
47 |
| Inference for extremal conditional quantile models, with an application to market and birthweight risks |
0 |
0 |
0 |
20 |
0 |
1 |
4 |
89 |
| Inference on Counterfactual Distributions |
0 |
0 |
3 |
23 |
0 |
1 |
8 |
144 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
893 |
1 |
2 |
4 |
1,921 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
434 |
0 |
0 |
1 |
937 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
113 |
0 |
0 |
3 |
351 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
1 |
0 |
0 |
3 |
4 |
| Inference on counterfactual distributions |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Low-rank approximations of nonseparable panel models |
0 |
0 |
2 |
4 |
0 |
0 |
4 |
12 |
| Low-rank approximations of nonseparable panel models |
0 |
0 |
0 |
1 |
0 |
2 |
3 |
15 |
| Marital Sorting, Household Inequality and Selection |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
6 |
| Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
0 |
0 |
41 |
0 |
1 |
1 |
40 |
| Network and Panel Quantile Effects Via Distribution Regression |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
12 |
| Network and panel quantile effects via distribution regression |
0 |
0 |
0 |
11 |
0 |
0 |
1 |
30 |
| Network and panel quantile effects via distribution regression |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
23 |
| Nonlinear Factor Models for Network and Panel Data |
0 |
0 |
0 |
12 |
0 |
0 |
2 |
57 |
| Nonlinear factor models for network and panel data |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
32 |
| Nonlinear factor models for network and panel data |
0 |
0 |
0 |
28 |
0 |
0 |
1 |
61 |
| Nonparametric Identification in Panels using Quantiles |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
13 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
59 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
23 |
0 |
0 |
1 |
39 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Nonseparable Sample Selection Models with Censored Selection Rules |
0 |
0 |
1 |
7 |
1 |
1 |
6 |
64 |
| Nonseparable Sample Selection Models with Censored Selection Rules: An Application to Wage Decompositions |
0 |
0 |
0 |
22 |
0 |
0 |
0 |
64 |
| Nonseparable multinomial choice models in cross-section and panel data |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
| Nonseparable multinomial choice models in cross-section and panel data |
0 |
0 |
0 |
15 |
0 |
0 |
0 |
24 |
| Nonseparable sample selection models with censored selection rules |
0 |
0 |
0 |
4 |
1 |
1 |
2 |
38 |
| Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
33 |
| Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference |
0 |
0 |
0 |
110 |
0 |
0 |
3 |
346 |
| 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 |
6 |
12 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
9 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
11 |
0 |
1 |
4 |
91 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
79 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
16 |
0 |
0 |
2 |
121 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
75 |
1 |
1 |
1 |
201 |
| Program evaluation with high-dimensional data |
0 |
0 |
0 |
0 |
0 |
1 |
5 |
7 |
| QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING |
0 |
0 |
0 |
71 |
0 |
0 |
2 |
339 |
| Quantile Regression under Misspecification |
0 |
0 |
0 |
2 |
0 |
3 |
7 |
455 |
| Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
0 |
0 |
2 |
287 |
1 |
1 |
6 |
946 |
| Quantile Regression with Censoring and Endogeneity |
0 |
0 |
1 |
112 |
0 |
0 |
2 |
364 |
| Quantile Regression with Censoring and Endogeneity |
0 |
1 |
2 |
6 |
0 |
1 |
3 |
110 |
| Quantile Regression with Censoring and Endogeneity |
0 |
0 |
1 |
58 |
0 |
1 |
2 |
189 |
| Quantile and Average Effects in Nonseparable Panel Models |
0 |
0 |
0 |
25 |
0 |
0 |
0 |
99 |
| Quantile and Probability Curves Without Crossing |
0 |
0 |
0 |
3 |
1 |
1 |
2 |
31 |
| Quantile and Probability Curves without Crossing |
0 |
0 |
0 |
18 |
0 |
0 |
3 |
141 |
| Quantile and Probability Curves without Crossing |
0 |
1 |
1 |
3 |
0 |
2 |
2 |
49 |
| Quantile and average effects in nonseparable panel models |
0 |
0 |
0 |
43 |
0 |
0 |
1 |
113 |
| Quantile and probability curves without crossing |
0 |
0 |
0 |
68 |
0 |
0 |
1 |
272 |
| Quantile regression with censoring and endogeneity |
0 |
0 |
0 |
40 |
0 |
0 |
3 |
139 |
| Quantreg.nonpar: an R package for performing nonparametric series quantile regression |
0 |
0 |
0 |
3 |
0 |
0 |
3 |
19 |
| Quantreg.nonpar: an R package for performing nonparametric series quantile regression |
0 |
0 |
0 |
19 |
0 |
0 |
1 |
131 |
| Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
0 |
1 |
2 |
2 |
3 |
| Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
| Rearranging Edgeworth-Cornish-Fisher Expansions |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
15 |
| Rearranging Edgeworth-Cornish-Fisher expansions |
0 |
0 |
0 |
90 |
0 |
0 |
0 |
331 |
| Selection and the Distribution of Female Hourly Wages in the U.S |
0 |
0 |
0 |
7 |
0 |
0 |
2 |
17 |
| Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
90 |
| Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
35 |
| Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
| The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
0 |
1 |
15 |
1 |
1 |
8 |
65 |
| The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
7 |
| The sorted effects method: discovering heterogeneous effects beyond their averages |
0 |
0 |
0 |
14 |
0 |
0 |
2 |
79 |
| probitfe and logitfe: Bias corrections for probit and logit models with two-way fixed effects |
0 |
0 |
2 |
46 |
0 |
0 |
14 |
116 |
| Total Working Papers |
2 |
8 |
76 |
5,712 |
37 |
101 |
480 |
19,331 |
| Journal Article |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| Average and Quantile Effects in Nonseparable Panel Models |
0 |
0 |
0 |
38 |
1 |
1 |
5 |
194 |
| Bias corrections for probit and logit models with two-way fixed effects |
0 |
0 |
3 |
77 |
0 |
0 |
12 |
296 |
| Bias corrections for two-step fixed effects panel data estimators |
0 |
1 |
4 |
164 |
0 |
1 |
9 |
525 |
| Censored quantile instrumental-variable estimation with Stata |
0 |
0 |
2 |
12 |
0 |
0 |
3 |
60 |
| Conditional quantile processes based on series or many regressors |
0 |
0 |
3 |
38 |
0 |
0 |
7 |
107 |
| Evaluating the role of income, state dependence and individual specific heterogeneity in the determination of subjective health assessments |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
18 |
| Fast algorithms for the quantile regression process |
1 |
1 |
2 |
6 |
1 |
2 |
9 |
27 |
| Fixed Effects Estimation of Large-TPanel Data Models |
0 |
1 |
3 |
19 |
2 |
7 |
18 |
93 |
| Fixed effects estimation of structural parameters and marginal effects in panel probit models |
2 |
4 |
8 |
356 |
5 |
8 |
22 |
1,029 |
| Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes |
0 |
0 |
1 |
10 |
0 |
0 |
5 |
47 |
| Household labor supply: evidence for Spain |
0 |
0 |
0 |
107 |
0 |
1 |
2 |
391 |
| Improving point and interval estimators of monotone functions by rearrangement |
0 |
0 |
0 |
34 |
0 |
0 |
3 |
138 |
| Individual and time effects in nonlinear panel models with large N, T |
0 |
5 |
19 |
246 |
4 |
14 |
61 |
840 |
| Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks |
0 |
0 |
1 |
36 |
1 |
4 |
12 |
162 |
| Inference on Counterfactual Distributions |
1 |
1 |
3 |
368 |
3 |
6 |
15 |
971 |
| Low-rank approximations of nonseparable panel models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
8 |
| Mastering Panel Metrics: Causal Impact of Democracy on Growth |
0 |
1 |
2 |
29 |
1 |
5 |
10 |
91 |
| Network and panel quantile effects via distribution regression |
0 |
0 |
2 |
4 |
0 |
2 |
9 |
12 |
| Nonlinear factor models for network and panel data |
0 |
1 |
4 |
39 |
0 |
1 |
10 |
110 |
| Nonparametric identification in panels using quantiles |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
93 |
| Nonseparable multinomial choice models in cross-section and panel data |
0 |
0 |
1 |
13 |
0 |
0 |
4 |
65 |
| Nonseparable sample selection models with censored selection rules |
0 |
0 |
0 |
1 |
0 |
1 |
4 |
5 |
| Panel data models with nonadditive unobserved heterogeneity: Estimation and inference |
0 |
0 |
0 |
23 |
0 |
0 |
4 |
104 |
| Parametric Modeling of Quantile Regression Coefficient Functions With Longitudinal Data |
0 |
1 |
1 |
5 |
0 |
2 |
2 |
15 |
| Philip G. Wright, directed acyclic graphs, and instrumental variables |
0 |
2 |
2 |
2 |
0 |
2 |
2 |
2 |
| Program Evaluation and Causal Inference With High‐Dimensional Data |
0 |
0 |
2 |
33 |
1 |
2 |
8 |
142 |
| Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure |
0 |
0 |
2 |
400 |
0 |
3 |
10 |
1,243 |
| Quantile and Probability Curves Without Crossing |
0 |
0 |
0 |
82 |
0 |
0 |
2 |
307 |
| Quantile regression with censoring and endogeneity |
0 |
0 |
5 |
89 |
0 |
1 |
14 |
399 |
| Rearranging Edgeworth–Cornish–Fisher expansions |
0 |
0 |
0 |
31 |
0 |
0 |
1 |
133 |
| Semiparametric estimation of structural functions in nonseparable triangular models |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
26 |
| The Consequences of Teenage Childbearing: Consistent Estimates When Abortion Makes Miscarriage Non‐random |
0 |
0 |
0 |
23 |
1 |
1 |
7 |
209 |
| The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages |
0 |
0 |
1 |
17 |
0 |
1 |
5 |
93 |
| Total Journal Articles |
4 |
18 |
71 |
2,319 |
21 |
67 |
280 |
7,955 |
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