| Working Paper |
File Downloads |
Abstract Views |
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3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Robust Method for Microforecasting and Estimation of Random Effects |
0 |
0 |
0 |
21 |
0 |
1 |
10 |
31 |
| A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent |
0 |
0 |
0 |
0 |
2 |
11 |
25 |
28 |
| A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent |
0 |
0 |
0 |
30 |
0 |
3 |
11 |
75 |
| A tale of two Koreas: property rights and fairness |
0 |
0 |
0 |
6 |
1 |
2 |
9 |
24 |
| Ability, sorting and wage inequality |
0 |
0 |
0 |
173 |
2 |
5 |
60 |
604 |
| An Econometric Perspective on Algorithmic Subsampling |
0 |
0 |
0 |
29 |
1 |
2 |
9 |
46 |
| An econometric perspective on algorithmic subsampling |
0 |
0 |
1 |
1 |
1 |
3 |
10 |
14 |
| Average Adjusted Association: Efficient Estimation with High Dimensional Confounders |
0 |
0 |
0 |
5 |
1 |
5 |
16 |
25 |
| Best Subset Binary Prediction |
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0 |
0 |
1 |
2 |
5 |
15 |
62 |
| Best subset binary prediction |
0 |
0 |
0 |
0 |
0 |
4 |
11 |
16 |
| Best subset binary prediction |
0 |
0 |
0 |
25 |
1 |
7 |
20 |
72 |
| Binary Classification with the Maximum Score Model and Linear Programming |
0 |
0 |
0 |
0 |
1 |
4 |
13 |
13 |
| Binary classification with the maximum score model and linear programming |
0 |
1 |
9 |
9 |
0 |
6 |
16 |
16 |
| Binary classification with the maximum score model and linear programming |
0 |
0 |
1 |
1 |
0 |
2 |
9 |
9 |
| Bounding Treatment Effects by Pooling Limited Information across Observations |
0 |
0 |
1 |
27 |
0 |
1 |
7 |
20 |
| Bounding the Effect of Persuasion with Monotonicity Assumptions: Reassessing the Impact of TV Debates |
0 |
0 |
1 |
1 |
0 |
6 |
15 |
15 |
| Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
31 |
0 |
2 |
6 |
94 |
| Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
0 |
2 |
4 |
14 |
16 |
| Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
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0 |
0 |
0 |
0 |
4 |
8 |
10 |
| Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
7 |
0 |
1 |
24 |
59 |
| Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions |
0 |
0 |
0 |
22 |
0 |
1 |
11 |
51 |
| Causal inference in case-control studies |
0 |
0 |
0 |
1 |
1 |
5 |
10 |
29 |
| Characterization of the Asymptotic Distribution of Semiparametric M-Estimators |
0 |
0 |
1 |
177 |
1 |
4 |
14 |
399 |
| Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
0 |
0 |
9 |
0 |
6 |
13 |
57 |
| Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
0 |
0 |
142 |
0 |
2 |
87 |
440 |
| DOUBLY ROBUST UNIFORM CONFIDENCE BAND FOR THE CONDITIONAL AVERAGE TREATMENT EFFECT FUNCTION |
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0 |
0 |
22 |
0 |
2 |
10 |
96 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
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0 |
0 |
2 |
0 |
1 |
7 |
17 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
2 |
1 |
2 |
13 |
37 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
7 |
0 |
5 |
17 |
68 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
22 |
0 |
1 |
10 |
54 |
| Do Institutions Affect Social Preferences? Evidence from Divided Korea |
0 |
0 |
0 |
86 |
1 |
5 |
15 |
196 |
| Do institutions affect social preferences? Evidence from divided Korea |
0 |
0 |
0 |
121 |
0 |
1 |
12 |
191 |
| Do institutions affect social preferences? Evidence from divided Korea |
0 |
0 |
1 |
2 |
1 |
8 |
17 |
24 |
| Does It Matter Who Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited |
0 |
0 |
0 |
33 |
1 |
4 |
13 |
140 |
| Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited |
0 |
0 |
0 |
9 |
0 |
4 |
9 |
72 |
| Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function |
0 |
0 |
0 |
4 |
1 |
4 |
11 |
34 |
| Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
0 |
2 |
2 |
9 |
13 |
| Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
50 |
2 |
6 |
11 |
149 |
| Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
8 |
2 |
6 |
17 |
56 |
| Empirical Bayes Estimation in Heterogeneous Coefficient Panel Models |
0 |
0 |
3 |
3 |
0 |
2 |
11 |
11 |
| Endogeneity in Quantile Regression Models: A Control Function Approach |
0 |
0 |
0 |
538 |
0 |
5 |
22 |
1,671 |
| Endogeneity in quantile regression models: a control function approach |
0 |
0 |
0 |
0 |
2 |
6 |
32 |
41 |
| Endogeneity in quantile regression models: a control function approach |
0 |
1 |
1 |
262 |
1 |
5 |
15 |
916 |
| Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality |
0 |
0 |
0 |
138 |
0 |
4 |
17 |
333 |
| Estimating panel data duration models with censored data |
0 |
0 |
0 |
0 |
1 |
2 |
6 |
15 |
| Estimating panel data duration models with censored data |
0 |
0 |
0 |
276 |
0 |
1 |
7 |
821 |
| Exact computation of GMM estimators for instrumental variable quantile regression models |
0 |
0 |
0 |
1 |
0 |
1 |
9 |
18 |
| Exact computation of GMM estimators for instrumental variable quantile regression models |
0 |
0 |
0 |
25 |
0 |
1 |
11 |
60 |
| Factor-Driven Two-Regime Regression |
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0 |
0 |
6 |
8 |
10 |
15 |
63 |
| Factor-Driven Two-Regime Regression |
0 |
0 |
0 |
59 |
0 |
1 |
11 |
121 |
| Factor-Driven Two-Regime Regression |
0 |
0 |
1 |
13 |
0 |
0 |
16 |
64 |
| Fast Inference for Quantile Regression with Tens of Millions of Observations |
0 |
0 |
0 |
14 |
0 |
6 |
17 |
34 |
| Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling |
0 |
0 |
0 |
19 |
1 |
4 |
7 |
46 |
| Filtered and Unfiltered Treatment Effects with Targeting Instruments |
0 |
0 |
0 |
1 |
2 |
3 |
10 |
26 |
| Group Shapley Value and Counterfactual Simulations in a Structural Model |
0 |
1 |
1 |
13 |
2 |
6 |
14 |
60 |
| High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization |
0 |
0 |
1 |
11 |
3 |
5 |
12 |
45 |
| Identification of a competing risks model with unknown transformations of latent failure times |
0 |
0 |
0 |
97 |
0 |
3 |
11 |
412 |
| Identifying Effects of Multivalued Treatments |
0 |
0 |
0 |
43 |
0 |
2 |
13 |
52 |
| Identifying Effects of Multivalued Treatments |
0 |
0 |
1 |
46 |
1 |
4 |
11 |
79 |
| Identifying effects of multivalued treatments |
0 |
0 |
0 |
1 |
1 |
11 |
22 |
29 |
| Identifying effects of multivalued treatments |
0 |
0 |
0 |
10 |
1 |
5 |
9 |
35 |
| Identifying effects of multivalued treatments |
0 |
0 |
1 |
6 |
0 |
0 |
5 |
37 |
| Identifying the Effect of Persuasion |
0 |
0 |
0 |
13 |
1 |
12 |
22 |
63 |
| Identifying the effect of persuasion |
0 |
0 |
0 |
2 |
0 |
2 |
7 |
47 |
| Identifying the effect of persuasion |
0 |
0 |
0 |
3 |
0 |
0 |
7 |
16 |
| Identifying the effect of persuasion |
0 |
0 |
0 |
2 |
0 |
2 |
7 |
19 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
0 |
0 |
11 |
75 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
2 |
3 |
16 |
129 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
1 |
1 |
13 |
18 |
| Implementing intersection bounds in Stata |
0 |
0 |
1 |
1 |
0 |
2 |
8 |
10 |
| Implicit Bias against a Capitalistic Society Predicts Market Earnings |
0 |
0 |
0 |
6 |
1 |
3 |
7 |
14 |
| Individual Shrinkage for Random Effects |
1 |
3 |
3 |
19 |
1 |
7 |
14 |
28 |
| Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds |
0 |
0 |
0 |
30 |
1 |
2 |
11 |
23 |
| Individual welfare analysis: Random quasilinear utility, independence, and confidence bounds |
0 |
1 |
1 |
4 |
0 |
3 |
13 |
17 |
| Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic |
0 |
0 |
0 |
46 |
0 |
3 |
10 |
48 |
| Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic |
0 |
1 |
1 |
1 |
1 |
4 |
13 |
16 |
| Inference in a class of optimization problems: Con?dence regions and ?nite sample bounds on errors in coverage probabilities |
0 |
0 |
0 |
10 |
0 |
2 |
9 |
19 |
| Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities |
0 |
0 |
0 |
17 |
0 |
3 |
6 |
27 |
| Institutions, Competitiveness and Cognitive Ability |
0 |
0 |
1 |
36 |
2 |
6 |
15 |
108 |
| Institutions, competitiveness and cognitive ability |
0 |
0 |
2 |
12 |
1 |
3 |
12 |
22 |
| International trends in technological progress: stylized facts from patent citations, 1980-2011 |
0 |
0 |
0 |
66 |
2 |
7 |
9 |
98 |
| International trends in technological progress: stylized facts from patent citations, 1980-2011 |
0 |
0 |
0 |
0 |
0 |
5 |
11 |
14 |
| Intersection Bounds: estimation and inference |
0 |
0 |
0 |
1 |
2 |
6 |
18 |
24 |
| Intersection Bounds: estimation and inference |
0 |
0 |
0 |
88 |
0 |
10 |
20 |
349 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
1 |
5 |
16 |
19 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
17 |
0 |
7 |
23 |
120 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
36 |
1 |
9 |
17 |
143 |
| Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
1 |
6 |
15 |
18 |
| Is Distance Dying at Last? |
0 |
0 |
0 |
2 |
0 |
1 |
9 |
46 |
| Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
34 |
0 |
5 |
11 |
170 |
| Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
59 |
0 |
2 |
11 |
245 |
| Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
66 |
1 |
2 |
10 |
249 |
| Is distance dying at last? |
0 |
0 |
0 |
7 |
0 |
3 |
7 |
72 |
| Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
51 |
1 |
6 |
11 |
195 |
| Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
2 |
0 |
0 |
7 |
71 |
| Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
1 |
107 |
0 |
2 |
8 |
141 |
| Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
0 |
1 |
0 |
3 |
9 |
12 |
| Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
0 |
41 |
1 |
3 |
15 |
68 |
| Learning the Effect of Persuasion via Difference-In-Differences |
0 |
0 |
0 |
10 |
1 |
1 |
6 |
44 |
| Learning the effect of persuasion via difference-in-differences |
0 |
1 |
2 |
2 |
0 |
4 |
11 |
15 |
| Least Squares Estimation Using Sketched Data with Heteroskedastic Errors |
0 |
0 |
0 |
20 |
0 |
2 |
18 |
55 |
| Leave No One Undermined: Policy Targeting with Regret Aversion |
0 |
1 |
1 |
1 |
0 |
2 |
8 |
8 |
| Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
13 |
1 |
4 |
14 |
150 |
| Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
49 |
0 |
4 |
18 |
191 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
1 |
1 |
7 |
10 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
0 |
2 |
41 |
121 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
31 |
1 |
2 |
9 |
146 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
1 |
9 |
12 |
| Maximum score estimation of preference parameters for a binary choice model under uncertainty |
0 |
0 |
0 |
1 |
0 |
3 |
9 |
14 |
| Maximum score estimation of preference parameters for a binary choice model under uncertainty |
0 |
0 |
0 |
48 |
0 |
1 |
8 |
223 |
| Maximum score estimation with nonparametrically generated regressors |
0 |
0 |
0 |
21 |
0 |
2 |
11 |
92 |
| Maximum score estimation with nonparametrically generated regressors |
0 |
0 |
0 |
1 |
4 |
7 |
15 |
24 |
| Nonparametric Estimation of an Additive Quantile Regression Model |
0 |
0 |
0 |
362 |
0 |
3 |
16 |
983 |
| Nonparametric Identification of Accelerated Failure Time Competing Risks Models |
0 |
0 |
0 |
42 |
1 |
3 |
12 |
165 |
| Nonparametric Tests of Conditional Treatment Effects |
0 |
0 |
0 |
141 |
2 |
5 |
12 |
500 |
| Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
1 |
0 |
0 |
8 |
54 |
| Nonparametric estimation and inference under shape restrictions |
0 |
0 |
2 |
2 |
1 |
3 |
8 |
9 |
| Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
43 |
0 |
1 |
9 |
119 |
| Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
0 |
0 |
2 |
7 |
9 |
| Nonparametric estimation of an additive quantile regression model |
0 |
0 |
0 |
0 |
0 |
2 |
13 |
17 |
| Nonparametric estimation of an additive quantile regression model |
0 |
0 |
0 |
168 |
0 |
2 |
8 |
490 |
| Nonparametric identification of accelerated failure time competing risks models |
0 |
0 |
0 |
41 |
0 |
1 |
4 |
133 |
| Nonparametric instrumental variables estimation of a quantile regression model |
0 |
0 |
0 |
235 |
2 |
4 |
13 |
677 |
| Nonparametric tests of conditional treatment effects |
0 |
0 |
0 |
31 |
0 |
4 |
11 |
156 |
| Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
39 |
1 |
6 |
16 |
50 |
| Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
54 |
0 |
4 |
12 |
98 |
| Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
11 |
0 |
3 |
9 |
44 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
1 |
0 |
3 |
15 |
18 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
40 |
1 |
6 |
12 |
58 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
76 |
0 |
3 |
7 |
45 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
0 |
0 |
2 |
5 |
6 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
0 |
0 |
3 |
14 |
16 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
91 |
0 |
0 |
10 |
54 |
| Oracle Estimation of a Change Point in High Dimensional Quantile Regression |
0 |
0 |
0 |
25 |
0 |
4 |
10 |
45 |
| Persuasion Effects in Regression Discontinuity Designs |
0 |
0 |
4 |
4 |
0 |
2 |
11 |
11 |
| Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
1 |
67 |
0 |
4 |
16 |
91 |
| Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
59 |
0 |
7 |
17 |
183 |
| Please call me John: name choice and the assimilation of immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
57 |
0 |
1 |
6 |
136 |
| Please call me John: name choice and the assimilation of immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
0 |
2 |
8 |
15 |
22 |
| Policy Learning with Confidence |
0 |
2 |
6 |
8 |
1 |
13 |
44 |
51 |
| Policy learning with confidence |
0 |
1 |
1 |
1 |
0 |
6 |
20 |
20 |
| Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors |
0 |
0 |
0 |
11 |
2 |
4 |
6 |
19 |
| Property Rights and Fairness: A Tale of Two Koreas |
0 |
0 |
0 |
20 |
0 |
4 |
19 |
70 |
| Recombinant innovation and the boundaries of the firm |
0 |
0 |
0 |
16 |
1 |
4 |
12 |
93 |
| Recombinant innovation and the boundaries of the firm |
0 |
0 |
0 |
0 |
0 |
2 |
7 |
10 |
| Reform of Unemployment Compensation in Germany: A Nonparametric Bounds Analysis Using Register Data |
0 |
0 |
0 |
30 |
1 |
10 |
21 |
281 |
| Reform of unemployment compensation in Germany: a nonparametric bounds analysis using register data |
0 |
0 |
0 |
83 |
1 |
6 |
12 |
602 |
| SEMIPARAMETRIC ESTIMATION OF A BINARYRESPONSE MODEL WITH A CHANGE-POINTDUE TO A COVARIATE THRESHOLD |
0 |
0 |
0 |
6 |
0 |
3 |
13 |
41 |
| SGMM: Stochastic Approximation to Generalized Method of Moments |
0 |
0 |
2 |
26 |
0 |
5 |
24 |
61 |
| SLIM: Stochastic Learning and Inference in Overidentified Models |
0 |
0 |
4 |
4 |
1 |
3 |
11 |
11 |
| SLIM: Stochastic Learning and Inference in Overidentified Models |
1 |
1 |
2 |
2 |
2 |
4 |
14 |
14 |
| Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects |
0 |
0 |
0 |
508 |
0 |
0 |
8 |
1,338 |
| Semiparametric estimation of a binary response model with a change-point due to a covariate threshold |
0 |
0 |
0 |
2 |
1 |
6 |
14 |
62 |
| Semiparametric estimation of a panel data proportional hazards model with fixed effects |
0 |
0 |
0 |
0 |
0 |
3 |
10 |
13 |
| Semiparametric estimation of a panel data proportional hazards model with fixed effects |
0 |
0 |
0 |
279 |
0 |
4 |
10 |
805 |
| Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
5 |
0 |
1 |
3 |
54 |
| Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
16 |
0 |
1 |
13 |
55 |
| Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
15 |
0 |
2 |
8 |
71 |
| Sparse HP filter: Finding kinks in the COVID-19 contact rate |
0 |
0 |
0 |
0 |
3 |
4 |
11 |
19 |
| Sparse Quantile Regression |
0 |
0 |
0 |
17 |
2 |
3 |
12 |
40 |
| Sparse Quantile Regression |
0 |
0 |
0 |
9 |
0 |
1 |
8 |
21 |
| TESTING FOR A GENERAL CLASS OF FUNCTIONAL INEQUALITIES |
0 |
0 |
0 |
54 |
0 |
7 |
23 |
111 |
| TESTING FOR STOCHASTICMONOTONICITY |
0 |
0 |
0 |
0 |
0 |
2 |
10 |
62 |
| Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative |
0 |
0 |
0 |
162 |
0 |
7 |
20 |
583 |
| Testing for a general class of functional inequalities |
0 |
0 |
0 |
20 |
1 |
8 |
14 |
107 |
| Testing for a general class of functional inequalities |
0 |
0 |
1 |
1 |
0 |
2 |
11 |
14 |
| Testing for stochastic monotonicity |
0 |
0 |
0 |
2 |
0 |
2 |
9 |
62 |
| Testing for stochastic monotonicity |
0 |
0 |
0 |
53 |
0 |
3 |
13 |
170 |
| Testing for threshold effects in regression models |
0 |
0 |
1 |
211 |
3 |
5 |
19 |
582 |
| Testing functional inequalities |
0 |
0 |
0 |
67 |
1 |
4 |
9 |
144 |
| The ET Interview: Professor Joel L. Horowitz |
0 |
0 |
0 |
22 |
2 |
3 |
10 |
41 |
| The identification power of smoothness assumptions in models with counterfactual outcomes |
0 |
0 |
0 |
27 |
0 |
2 |
13 |
100 |
| The identification power of smoothness assumptions in models with counterfactual outcomes |
0 |
0 |
0 |
0 |
0 |
3 |
10 |
12 |
| The lasso for high-dimensional regression with a possible change-point |
0 |
0 |
0 |
33 |
1 |
3 |
10 |
237 |
| The lasso for high-dimensional regression with a possible change-point |
0 |
0 |
0 |
0 |
1 |
6 |
17 |
17 |
| Treatment Choice with Nonlinear Regret |
0 |
0 |
0 |
7 |
0 |
1 |
6 |
20 |
| Treatment Choice, Mean Square Regret and Partial Identification |
0 |
0 |
0 |
9 |
2 |
4 |
9 |
20 |
| Treatment Effects with Targeting Instruments |
0 |
0 |
0 |
5 |
0 |
2 |
13 |
31 |
| Treatment effects with targeting instruments |
0 |
0 |
0 |
0 |
1 |
3 |
12 |
14 |
| Trends in Quality Adjusted Skill Premia in the US, 1960-2000 |
0 |
0 |
0 |
30 |
0 |
1 |
5 |
100 |
| Trends in Quality-Adjusted Skill Premia in the United States, 1960-2000 |
0 |
1 |
1 |
86 |
0 |
6 |
16 |
216 |
| Trends in quality-adjusted skill premia in the United States, 1960-2000 |
0 |
0 |
0 |
323 |
2 |
5 |
13 |
603 |
| Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
29 |
0 |
2 |
7 |
77 |
| Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
74 |
0 |
0 |
10 |
231 |
| Why North Korean Refugees are Reluctant to Compete: The Roles of Cognitive Ability |
0 |
0 |
3 |
20 |
3 |
12 |
33 |
63 |
| Total Working Papers |
2 |
15 |
65 |
7,575 |
121 |
678 |
2,437 |
23,776 |
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