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12 months |
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Last month |
3 months |
12 months |
Total |

A Robust Method for Microforecasting and Estimation of Random Effects |
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0 |
21 |
21 |
0 |
0 |
17 |
17 |

A Robust Method for Microforecasting and Estimation of Random Effects |
1 |
1 |
16 |
16 |
1 |
3 |
13 |
13 |

A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent |
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0 |
0 |
30 |
0 |
0 |
1 |
64 |

A tale of two Koreas: property rights and fairness |
0 |
0 |
0 |
6 |
0 |
1 |
1 |
13 |

Ability, sorting and wage inequality |
0 |
0 |
2 |
173 |
0 |
0 |
3 |
541 |

An Econometric Perspective on Algorithmic Subsampling |
0 |
0 |
0 |
28 |
0 |
2 |
3 |
35 |

An econometric perspective on algorithmic subsampling |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
4 |

Average Adjusted Association: Efficient Estimation with High Dimensional Confounders |
0 |
0 |
0 |
5 |
0 |
1 |
4 |
9 |

Best Subset Binary Prediction |
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0 |
0 |
1 |
0 |
0 |
6 |
43 |

Best subset binary prediction |
0 |
0 |
0 |
25 |
0 |
0 |
2 |
49 |

Bounding Treatment Effects by Pooling Limited Information across Observations |
0 |
0 |
0 |
25 |
1 |
1 |
3 |
12 |

Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
34 |

Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
31 |
0 |
0 |
0 |
84 |

Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions |
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0 |
1 |
22 |
0 |
2 |
7 |
35 |

Causal inference in case-control studies |
0 |
0 |
0 |
1 |
1 |
2 |
3 |
17 |

Characterization of the Asymptotic Distribution of Semiparametric M-Estimators |
0 |
0 |
0 |
176 |
0 |
1 |
1 |
381 |

Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
0 |
0 |
8 |
0 |
0 |
0 |
41 |

Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
0 |
0 |
142 |
0 |
0 |
0 |
352 |

DOUBLY ROBUST UNIFORM CONFIDENCE BAND FOR THE CONDITIONAL AVERAGE TREATMENT EFFECT FUNCTION |
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0 |
0 |
21 |
0 |
0 |
2 |
83 |

Desperate times call for desperate measures: government spending multipliers in hard times |
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1 |
1 |
21 |
0 |
1 |
2 |
42 |

Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
1 |
1 |
2 |
0 |
1 |
1 |
8 |

Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
21 |

Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
7 |
0 |
0 |
2 |
51 |

Do Institutions Affect Social Preferences? Evidence from Divided Korea |
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0 |
0 |
85 |
0 |
0 |
6 |
179 |

Do institutions affect social preferences? Evidence from divided Korea |
0 |
0 |
0 |
121 |
0 |
0 |
1 |
177 |

Does It Matter Who Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited |
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0 |
0 |
33 |
0 |
0 |
1 |
126 |

Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited |
0 |
0 |
0 |
9 |
0 |
0 |
0 |
59 |

Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
20 |

Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
36 |

Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
50 |
0 |
0 |
1 |
138 |

Endogeneity in Quantile Regression Models: A Control Function Approach |
0 |
0 |
1 |
538 |
2 |
3 |
10 |
1,645 |

Endogeneity in quantile regression models: a control function approach |
0 |
0 |
1 |
261 |
1 |
2 |
3 |
898 |

Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality |
0 |
0 |
1 |
138 |
0 |
0 |
3 |
312 |

Estimating panel data duration models with censored data |
0 |
0 |
0 |
276 |
0 |
0 |
0 |
813 |

Exact computation of GMM estimators for instrumental variable quantile regression models |
0 |
0 |
1 |
24 |
0 |
0 |
2 |
44 |

Factor-Driven Two-Regime Regression |
0 |
0 |
0 |
6 |
0 |
0 |
2 |
47 |

Factor-Driven Two-Regime Regression |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
45 |

Factor-Driven Two-Regime Regression |
0 |
0 |
1 |
58 |
0 |
1 |
7 |
109 |

Fast Inference for Quantile Regression with Tens of Millions of Observations |
0 |
0 |
2 |
13 |
0 |
0 |
7 |
13 |

Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling |
0 |
0 |
2 |
19 |
0 |
0 |
9 |
35 |

Filtered and Unfiltered Treatment Effects with Targeting Instruments |
0 |
1 |
1 |
1 |
0 |
2 |
4 |
13 |

High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization |
0 |
0 |
0 |
10 |
0 |
0 |
2 |
32 |

Identification of a competing risks model with unknown transformations of latent failure times |
0 |
0 |
0 |
97 |
0 |
0 |
0 |
399 |

Identifying Effects of Multivalued Treatments |
0 |
0 |
0 |
45 |
0 |
1 |
4 |
66 |

Identifying Effects of Multivalued Treatments |
0 |
0 |
1 |
42 |
0 |
1 |
2 |
36 |

Identifying effects of multivalued treatments |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
31 |

Identifying effects of multivalued treatments |
0 |
0 |
0 |
10 |
0 |
0 |
0 |
25 |

Identifying the Effect of Persuasion |
0 |
0 |
0 |
12 |
0 |
0 |
2 |
38 |

Identifying the effect of persuasion |
0 |
0 |
0 |
2 |
0 |
0 |
2 |
11 |

Identifying the effect of persuasion |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
39 |

Identifying the effect of persuasion |
0 |
0 |
1 |
3 |
0 |
0 |
4 |
7 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
0 |
1 |
1 |
113 |

Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
0 |
1 |
4 |
64 |

Implicit Bias against a Capitalistic Society Predicts Market Earnings |
0 |
0 |
4 |
4 |
0 |
0 |
3 |
3 |

Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds |
0 |
0 |
29 |
29 |
0 |
1 |
6 |
6 |

Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic |
0 |
0 |
2 |
46 |
0 |
3 |
9 |
30 |

Inference in a class of optimization problems: Con?dence regions and ?nite sample bounds on errors in coverage probabilities |
0 |
0 |
0 |
10 |
0 |
0 |
0 |
6 |

Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
19 |

Institutions, Competitiveness and Cognitive Ability |
0 |
0 |
3 |
34 |
1 |
1 |
6 |
90 |

Institutions, competitiveness and cognitive ability |
0 |
0 |
1 |
8 |
0 |
0 |
3 |
8 |

International trends in technological progress: stylized facts from patent citations, 1980-2011 |
0 |
0 |
0 |
66 |
0 |
0 |
0 |
88 |

Intersection Bounds: estimation and inference |
0 |
0 |
0 |
87 |
0 |
0 |
3 |
325 |

Intersection bounds: estimation and inference |
0 |
0 |
1 |
17 |
0 |
1 |
3 |
95 |

Intersection bounds: estimation and inference |
0 |
0 |
0 |
36 |
1 |
1 |
2 |
124 |

Is Distance Dying at Last? |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
31 |

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
1 |
66 |
0 |
1 |
3 |
238 |

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
34 |
1 |
1 |
1 |
157 |

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
59 |
0 |
0 |
0 |
234 |

Is distance dying at last? |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
64 |

Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
63 |

Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
1 |
51 |
0 |
0 |
2 |
183 |

Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
1 |
41 |
0 |
0 |
4 |
52 |

Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
2 |
106 |
0 |
0 |
7 |
132 |

Least Squares Estimation Using Sketched Data with Heteroskedastic Errors |
0 |
0 |
2 |
20 |
0 |
1 |
5 |
35 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
49 |
0 |
2 |
2 |
171 |

Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
12 |
0 |
1 |
1 |
132 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
31 |
0 |
1 |
2 |
123 |

Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
0 |
1 |
1 |
80 |

Maximum score estimation of preference parameters for a binary choice model under uncertainty |
0 |
0 |
0 |
48 |
0 |
0 |
11 |
212 |

Maximum score estimation with nonparametrically generated regressors |
0 |
0 |
0 |
21 |
0 |
0 |
1 |
78 |

Nonparametric Estimation of an Additive Quantile Regression Model |
0 |
0 |
0 |
362 |
0 |
1 |
3 |
962 |

Nonparametric Identification of Accelerated Failure Time Competing Risks Models |
0 |
0 |
0 |
42 |
1 |
1 |
2 |
146 |

Nonparametric Tests of Conditional Treatment Effects |
0 |
1 |
1 |
141 |
0 |
1 |
4 |
486 |

Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
1 |
0 |
1 |
2 |
46 |

Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
43 |
0 |
1 |
4 |
109 |

Nonparametric estimation of an additive quantile regression model |
0 |
0 |
0 |
168 |
0 |
1 |
2 |
477 |

Nonparametric identification of accelerated failure time competing risks models |
0 |
0 |
0 |
41 |
0 |
0 |
0 |
128 |

Nonparametric instrumental variables estimation of a quantile regression model |
0 |
0 |
0 |
235 |
0 |
0 |
1 |
663 |

Nonparametric tests of conditional treatment effects |
0 |
0 |
0 |
31 |
0 |
0 |
0 |
142 |

Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
54 |
0 |
0 |
1 |
85 |

Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
11 |
0 |
0 |
0 |
32 |

Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
39 |
0 |
1 |
1 |
32 |

Optimal data collection for randomized control trials |
0 |
0 |
0 |
76 |
0 |
0 |
0 |
38 |

Optimal data collection for randomized control trials |
0 |
0 |
0 |
91 |
0 |
0 |
0 |
44 |

Optimal data collection for randomized control trials |
0 |
0 |
0 |
40 |
0 |
0 |
1 |
45 |

Oracle Estimation of a Change Point in High Dimensional Quantile Regression |
0 |
0 |
0 |
25 |
0 |
0 |
3 |
34 |

Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
58 |
0 |
0 |
5 |
163 |

Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
1 |
66 |
0 |
0 |
2 |
71 |

Please call me John: name choice and the assimilation of immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
57 |
1 |
1 |
2 |
126 |

Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors |
0 |
1 |
10 |
10 |
0 |
1 |
10 |
10 |

Property Rights and Fairness: A Tale of Two Koreas |
0 |
0 |
0 |
20 |
1 |
1 |
2 |
50 |

Recombinant innovation and the boundaries of the firm |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
79 |

Reform of Unemployment Compensation in Germany: A Nonparametric Bounds Analysis Using Register Data |
0 |
0 |
0 |
30 |
0 |
1 |
1 |
260 |

Reform of unemployment compensation in Germany: a nonparametric bounds analysis using register data |
0 |
0 |
0 |
83 |
0 |
0 |
0 |
589 |

SEMIPARAMETRIC ESTIMATION OF A BINARYRESPONSE MODEL WITH A CHANGE-POINTDUE TO A COVARIATE THRESHOLD |
0 |
0 |
1 |
6 |
0 |
0 |
1 |
28 |

SGMM: Stochastic Approximation to Generalized Method of Moments |
0 |
2 |
24 |
24 |
3 |
7 |
19 |
19 |

Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects |
0 |
0 |
0 |
508 |
0 |
0 |
2 |
1,329 |

Semiparametric estimation of a binary response model with a change-point due to a covariate threshold |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
47 |

Semiparametric estimation of a panel data proportional hazards model with fixed effects |
0 |
0 |
0 |
279 |
0 |
0 |
1 |
794 |

Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
49 |

Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
14 |
0 |
1 |
2 |
60 |

Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
41 |

Sparse HP filter: Finding kinks in the COVID-19 contact rate |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
7 |

Sparse Quantile Regression |
0 |
0 |
1 |
8 |
0 |
2 |
3 |
10 |

Sparse Quantile Regression |
0 |
0 |
1 |
17 |
1 |
3 |
5 |
27 |

TESTING FOR A GENERAL CLASS OF FUNCTIONAL INEQUALITIES |
0 |
0 |
0 |
54 |
0 |
0 |
0 |
84 |

TESTING FOR STOCHASTICMONOTONICITY |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
51 |

Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative |
0 |
0 |
0 |
162 |
0 |
0 |
0 |
560 |

Testing for a general class of functional inequalities |
0 |
0 |
0 |
20 |
0 |
1 |
2 |
93 |

Testing for stochastic monotonicity |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
53 |

Testing for stochastic monotonicity |
0 |
0 |
0 |
53 |
0 |
0 |
0 |
157 |

Testing for threshold effects in regression models |
0 |
0 |
0 |
207 |
0 |
0 |
0 |
554 |

Testing functional inequalities |
0 |
0 |
0 |
67 |
0 |
0 |
0 |
134 |

The identification power of smoothness assumptions in models with counterfactual outcomes |
0 |
0 |
0 |
27 |
0 |
0 |
1 |
87 |

The lasso for high-dimensional regression with a possible change-point |
0 |
0 |
0 |
33 |
1 |
2 |
14 |
224 |

Treatment Choice with Nonlinear Regret |
0 |
0 |
1 |
6 |
0 |
2 |
4 |
11 |

Treatment Choice, Mean Square Regret and Partial Identification |
0 |
0 |
8 |
8 |
0 |
1 |
8 |
8 |

Treatment Effects with Targeting Instruments |
0 |
0 |
1 |
3 |
0 |
0 |
1 |
13 |

Trends in Quality Adjusted Skill Premia in the US, 1960-2000 |
0 |
0 |
0 |
30 |
0 |
0 |
0 |
93 |

Trends in Quality-Adjusted Skill Premia in the United States, 1960-2000 |
0 |
0 |
0 |
85 |
0 |
0 |
1 |
196 |

Trends in quality-adjusted skill premia in the United States, 1960-2000 |
0 |
0 |
0 |
323 |
0 |
0 |
0 |
586 |

Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
1 |
72 |
0 |
1 |
2 |
219 |

Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
27 |
0 |
1 |
2 |
68 |

Why North Korean Refugees are Reluctant to Compete: The Roles of Cognitive Ability |
0 |
0 |
1 |
14 |
0 |
0 |
1 |
20 |

Total Working Papers |
1 |
8 |
152 |
7,413 |
17 |
74 |
349 |
20,737 |