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12 months |
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Last month |
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12 months |
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A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent |
0 |
0 |
0 |
27 |
0 |
0 |
3 |
49 |

Ability, sorting and wage inequality |
0 |
0 |
0 |
171 |
0 |
2 |
3 |
529 |

Best Subset Binary Prediction |
0 |
0 |
0 |
1 |
0 |
5 |
11 |
20 |

Best subset binary prediction |
0 |
0 |
0 |
24 |
0 |
3 |
16 |
37 |

Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
1 |
7 |
1 |
4 |
12 |
19 |

Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
31 |
1 |
1 |
6 |
74 |

Characterization of the Asymptotic Distribution of Semiparametric M-Estimators |
0 |
1 |
1 |
175 |
0 |
3 |
7 |
369 |

Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
0 |
0 |
5 |
1 |
2 |
6 |
28 |

Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
1 |
1 |
142 |
1 |
3 |
5 |
339 |

DOUBLY ROBUST UNIFORM CONFIDENCE BAND FOR THE CONDITIONAL AVERAGE TREATMENT EFFECT FUNCTION |
0 |
0 |
0 |
20 |
0 |
4 |
8 |
65 |

Do Institutions Affect Social Preferences? Evidence from Divided Korea |
0 |
0 |
1 |
82 |
0 |
2 |
8 |
113 |

Do institutions affect social preferences? Evidence from divided Korea |
0 |
0 |
1 |
114 |
0 |
6 |
25 |
135 |

Does It Matter Who Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited |
0 |
0 |
1 |
31 |
0 |
1 |
7 |
116 |

Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited |
0 |
0 |
0 |
8 |
0 |
1 |
7 |
51 |

Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function |
0 |
0 |
1 |
2 |
0 |
0 |
3 |
7 |

Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
49 |
2 |
5 |
10 |
121 |

Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
7 |
1 |
3 |
13 |
25 |

Endogeneity in Quantile Regression Models: A Control Function Approach |
1 |
2 |
7 |
524 |
4 |
9 |
23 |
1,597 |

Endogeneity in quantile regression models: a control function approach |
0 |
0 |
2 |
257 |
1 |
2 |
10 |
877 |

Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality |
0 |
0 |
1 |
132 |
0 |
0 |
5 |
292 |

Estimating panel data duration models with censored data |
0 |
1 |
1 |
276 |
0 |
1 |
4 |
809 |

Exact computation of GMM estimators for instrumental variable quantile regression models |
0 |
0 |
0 |
23 |
1 |
3 |
8 |
21 |

Factor-Driven Two-Regime Regression |
0 |
1 |
7 |
45 |
0 |
11 |
27 |
46 |

Factor-Driven Two-Regime Regression |
0 |
0 |
2 |
3 |
0 |
6 |
13 |
19 |

High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization |
0 |
0 |
2 |
10 |
0 |
2 |
15 |
22 |

Identification of a competing risks model with unknown transformations of latent failure times |
0 |
0 |
0 |
97 |
0 |
0 |
2 |
387 |

Identifying Effects of Multivalued Treatments |
0 |
0 |
0 |
45 |
0 |
1 |
5 |
46 |

Identifying Effects of Multivalued Treatments |
0 |
0 |
2 |
39 |
1 |
4 |
11 |
25 |

Identifying effects of multivalued treatments |
0 |
0 |
3 |
5 |
0 |
1 |
8 |
12 |

Identifying effects of multivalued treatments |
0 |
0 |
0 |
10 |
0 |
4 |
6 |
19 |

Identifying the Effect of Persuasion |
0 |
0 |
3 |
10 |
0 |
4 |
7 |
11 |

Identifying the effect of persuasion |
0 |
0 |
0 |
1 |
1 |
4 |
13 |
18 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
0 |
4 |
8 |
49 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
1 |
7 |
9 |
98 |

International trends in technological progress: stylized facts from patent citations, 1980-2011 |
0 |
0 |
2 |
61 |
0 |
1 |
5 |
76 |

Intersection Bounds: estimation and inference |
0 |
0 |
1 |
84 |
0 |
4 |
7 |
309 |

Intersection bounds: estimation and inference |
0 |
0 |
0 |
33 |
0 |
2 |
13 |
108 |

Intersection bounds: estimation and inference |
0 |
0 |
1 |
14 |
0 |
5 |
11 |
86 |

Is Distance Dying at Last? |
0 |
0 |
0 |
1 |
0 |
4 |
5 |
12 |

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
65 |
0 |
3 |
10 |
213 |

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
57 |
0 |
1 |
8 |
206 |

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
34 |
0 |
2 |
9 |
141 |

Is distance dying at last? |
0 |
0 |
0 |
7 |
0 |
1 |
3 |
53 |

Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
2 |
49 |
0 |
1 |
13 |
159 |

Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
1 |
2 |
1 |
3 |
12 |
45 |

Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
0 |
0 |
2 |
7 |
53 |

Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
2 |
8 |
16 |
97 |
4 |
12 |
32 |
87 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
1 |
12 |
2 |
5 |
10 |
122 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
48 |
1 |
2 |
4 |
164 |

Local identification of nonparametric and semiparametric models |
0 |
1 |
1 |
31 |
1 |
3 |
8 |
100 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
1 |
16 |
2 |
3 |
8 |
69 |

Maximum score estimation of preference parameters for a binary choice model under uncertainty |
0 |
0 |
0 |
48 |
1 |
3 |
9 |
142 |

Maximum score estimation with nonparametrically generated regressors |
0 |
0 |
0 |
18 |
0 |
1 |
6 |
54 |

Non-Asymptotic Inference in a Class of Optimization Problems |
0 |
0 |
15 |
15 |
0 |
5 |
8 |
8 |

Nonparametric Estimation of an Additive Quantile Regression Model |
0 |
0 |
0 |
361 |
0 |
1 |
2 |
945 |

Nonparametric Identification of Accelerated Failure Time Competing Risks Models |
0 |
0 |
0 |
40 |
0 |
2 |
5 |
129 |

Nonparametric Tests of Conditional Treatment Effects |
0 |
0 |
6 |
134 |
3 |
9 |
32 |
434 |

Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
43 |
0 |
1 |
4 |
71 |

Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
1 |
1 |
1 |
3 |
18 |

Nonparametric estimation of an additive quantile regression model |
0 |
0 |
0 |
165 |
0 |
0 |
1 |
452 |

Nonparametric identification of accelerated failure time competing risks models |
0 |
0 |
0 |
41 |
0 |
1 |
7 |
116 |

Nonparametric instrumental variables estimation of a quantile regression model |
0 |
0 |
0 |
233 |
0 |
0 |
2 |
652 |

Nonparametric tests of conditional treatment effects |
0 |
0 |
0 |
29 |
0 |
3 |
8 |
125 |

Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
50 |
1 |
1 |
8 |
50 |

Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
39 |
1 |
2 |
7 |
16 |

Optimal data collection for randomized control trials |
0 |
0 |
1 |
76 |
2 |
2 |
7 |
19 |

Optimal data collection for randomized control trials |
0 |
0 |
0 |
91 |
2 |
2 |
5 |
25 |

Optimal data collection for randomized control trials |
0 |
1 |
3 |
40 |
2 |
9 |
17 |
28 |

Oracle Estimation of a Change Point in High Dimensional Quantile Regression |
0 |
0 |
1 |
25 |
0 |
6 |
10 |
17 |

Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
5 |
65 |
1 |
1 |
10 |
51 |

Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
1 |
5 |
57 |
0 |
6 |
22 |
77 |

Please call me John: name choice and the assimilation of immigrants in the United States, 1900-1930 |
0 |
0 |
4 |
55 |
1 |
3 |
17 |
103 |

Recombinant innovation and the boundaries of the firm |
0 |
0 |
0 |
16 |
0 |
1 |
5 |
54 |

Reform of Unemployment Compensation in Germany: A Nonparametric Bounds Analysis Using Register Data |
0 |
0 |
0 |
28 |
0 |
1 |
8 |
245 |

Reform of unemployment compensation in Germany: a nonparametric bounds analysis using register data |
0 |
0 |
0 |
82 |
0 |
3 |
5 |
582 |

SEMIPARAMETRIC ESTIMATION OF A BINARYRESPONSE MODEL WITH A CHANGE-POINTDUE TO A COVARIATE THRESHOLD |
0 |
0 |
0 |
4 |
0 |
1 |
3 |
20 |

Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects |
0 |
0 |
1 |
506 |
0 |
3 |
8 |
1,313 |

Semiparametric estimation of a binary response model with a change-point due to a covariate threshold |
0 |
0 |
0 |
2 |
0 |
1 |
2 |
35 |

Semiparametric estimation of a panel data proportional hazards model with fixed effects |
0 |
0 |
0 |
278 |
0 |
1 |
2 |
788 |

TESTING FOR A GENERAL CLASS OF FUNCTIONAL INEQUALITIES |
0 |
0 |
0 |
54 |
0 |
1 |
3 |
70 |

TESTING FOR STOCHASTICMONOTONICITY |
0 |
0 |
0 |
0 |
0 |
1 |
5 |
31 |

Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative |
0 |
0 |
1 |
162 |
0 |
1 |
3 |
556 |

Testing for a general class of functional inequalities |
0 |
0 |
0 |
20 |
0 |
5 |
8 |
75 |

Testing for stochastic monotonicity |
0 |
0 |
0 |
1 |
0 |
2 |
5 |
34 |

Testing for stochastic monotonicity |
0 |
0 |
0 |
52 |
1 |
1 |
5 |
138 |

Testing for threshold effects in regression models |
0 |
0 |
4 |
198 |
0 |
1 |
15 |
526 |

Testing functional inequalities |
0 |
0 |
0 |
66 |
0 |
2 |
4 |
124 |

The identification power of smoothness assumptions in models with counterfactual outcomes |
0 |
0 |
0 |
27 |
0 |
0 |
1 |
75 |

The lasso for high-dimensional regression with a possible change-point |
0 |
0 |
1 |
31 |
0 |
2 |
10 |
112 |

Trends in Quality Adjusted Skill Premia in the US, 1960-2000 |
0 |
0 |
0 |
30 |
0 |
1 |
3 |
85 |

Trends in Quality-Adjusted Skill Premia in the United States, 1960-2000 |
0 |
0 |
0 |
84 |
0 |
1 |
6 |
174 |

Trends in quality-adjusted skill premia in the United States, 1960-2000 |
0 |
0 |
0 |
323 |
0 |
0 |
8 |
570 |

Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
71 |
0 |
1 |
4 |
205 |

Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
27 |
1 |
1 |
4 |
63 |

Total Working Papers |
3 |
17 |
110 |
6,703 |
44 |
252 |
796 |
17,855 |