| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent |
0 |
0 |
0 |
30 |
0 |
0 |
1 |
63 |
| A tale of two Koreas: property rights and fairness |
0 |
1 |
2 |
6 |
1 |
2 |
5 |
12 |
| Ability, sorting and wage inequality |
0 |
0 |
0 |
171 |
0 |
0 |
1 |
538 |
| An Econometric Perspective on Algorithmic Subsampling |
1 |
1 |
2 |
28 |
1 |
1 |
2 |
32 |
| An econometric perspective on algorithmic subsampling |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Average Adjusted Association: Efficient Estimation with High Dimensional Confounders |
0 |
0 |
5 |
5 |
0 |
1 |
4 |
4 |
| Best Subset Binary Prediction |
0 |
0 |
0 |
1 |
1 |
1 |
5 |
35 |
| Best subset binary prediction |
0 |
0 |
0 |
25 |
0 |
1 |
1 |
46 |
| Bounding Treatment Effects by Pooling Limited Information across Observations |
0 |
0 |
25 |
25 |
0 |
0 |
7 |
7 |
| Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
31 |
0 |
0 |
0 |
84 |
| Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
33 |
| Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions |
0 |
0 |
2 |
21 |
0 |
0 |
4 |
28 |
| Causal inference in case-control studies |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
10 |
| Characterization of the Asymptotic Distribution of Semiparametric M-Estimators |
0 |
1 |
1 |
176 |
0 |
1 |
3 |
380 |
| Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
0 |
0 |
8 |
0 |
1 |
1 |
40 |
| Characterization of the asymptotic distribution of semiparametric M-estimators |
0 |
0 |
0 |
142 |
0 |
0 |
0 |
351 |
| DOUBLY ROBUST UNIFORM CONFIDENCE BAND FOR THE CONDITIONAL AVERAGE TREATMENT EFFECT FUNCTION |
0 |
0 |
0 |
21 |
0 |
0 |
1 |
81 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
7 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
2 |
0 |
1 |
2 |
21 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
20 |
0 |
0 |
2 |
40 |
| Desperate times call for desperate measures: government spending multipliers in hard times |
0 |
0 |
0 |
6 |
0 |
2 |
6 |
47 |
| Do Institutions Affect Social Preferences? Evidence from Divided Korea |
0 |
0 |
2 |
85 |
0 |
4 |
18 |
166 |
| Do institutions affect social preferences? Evidence from divided Korea |
0 |
0 |
2 |
119 |
0 |
1 |
7 |
171 |
| Does It Matter Who Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited |
1 |
1 |
1 |
33 |
1 |
1 |
1 |
125 |
| Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited |
0 |
0 |
1 |
9 |
0 |
0 |
2 |
59 |
| Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function |
0 |
1 |
1 |
3 |
0 |
1 |
2 |
19 |
| Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
7 |
0 |
1 |
1 |
36 |
| Doubly robust uniform confidence band for the conditional average treatment effect function |
0 |
0 |
0 |
49 |
0 |
0 |
0 |
136 |
| Endogeneity in Quantile Regression Models: A Control Function Approach |
2 |
3 |
5 |
536 |
2 |
3 |
7 |
1,633 |
| Endogeneity in quantile regression models: a control function approach |
0 |
0 |
0 |
259 |
0 |
0 |
5 |
894 |
| Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality |
0 |
0 |
0 |
136 |
0 |
0 |
1 |
308 |
| Estimating panel data duration models with censored data |
0 |
0 |
0 |
276 |
0 |
0 |
2 |
812 |
| Exact computation of GMM estimators for instrumental variable quantile regression models |
0 |
0 |
0 |
23 |
2 |
2 |
7 |
42 |
| Factor-Driven Two-Regime Regression |
0 |
0 |
0 |
12 |
1 |
1 |
12 |
43 |
| Factor-Driven Two-Regime Regression |
0 |
0 |
2 |
6 |
1 |
1 |
3 |
45 |
| Factor-Driven Two-Regime Regression |
0 |
0 |
3 |
56 |
2 |
2 |
15 |
101 |
| Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling |
0 |
0 |
3 |
15 |
1 |
3 |
10 |
20 |
| Filtered and Unfiltered Treatment Effects with Targeting Instruments |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
9 |
| Filtered and Unfiltered Treatment Effects with Targeting Instruments |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
12 |
| High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization |
0 |
0 |
0 |
10 |
0 |
1 |
2 |
30 |
| Identification of a competing risks model with unknown transformations of latent failure times |
0 |
0 |
0 |
97 |
1 |
2 |
4 |
399 |
| Identifying Effects of Multivalued Treatments |
0 |
0 |
1 |
41 |
0 |
0 |
4 |
33 |
| Identifying Effects of Multivalued Treatments |
0 |
0 |
0 |
45 |
0 |
0 |
1 |
61 |
| Identifying effects of multivalued treatments |
0 |
0 |
0 |
10 |
0 |
0 |
1 |
25 |
| Identifying effects of multivalued treatments |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
29 |
| Identifying the Effect of Persuasion |
0 |
0 |
1 |
12 |
0 |
1 |
4 |
35 |
| Identifying the effect of persuasion |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
8 |
| Identifying the effect of persuasion |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
37 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
1 |
1 |
2 |
60 |
| Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
1 |
2 |
4 |
112 |
| Inference for parameters identified by conditional moment restrictions using a penalized Bierens maximum statistic |
0 |
0 |
2 |
42 |
0 |
2 |
6 |
18 |
| Inference in a class of optimization problems: Con?dence regions and ?nite sample bounds on errors in coverage probabilities |
0 |
0 |
10 |
10 |
0 |
0 |
5 |
5 |
| Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities |
1 |
1 |
1 |
17 |
1 |
1 |
1 |
19 |
| Institutions, Competitiveness and Cognitive Ability |
0 |
0 |
0 |
30 |
0 |
0 |
7 |
82 |
| Institutions, competitiveness and cognitive ability |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
5 |
| International trends in technological progress: stylized facts from patent citations, 1980-2011 |
0 |
0 |
0 |
66 |
1 |
1 |
1 |
88 |
| Intersection Bounds: estimation and inference |
0 |
1 |
2 |
87 |
0 |
1 |
3 |
321 |
| Intersection bounds: estimation and inference |
0 |
0 |
1 |
16 |
0 |
0 |
2 |
91 |
| Intersection bounds: estimation and inference |
0 |
0 |
1 |
36 |
0 |
0 |
2 |
122 |
| Is Distance Dying at Last? |
0 |
0 |
0 |
1 |
1 |
1 |
7 |
29 |
| Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
1 |
59 |
0 |
0 |
4 |
234 |
| Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
34 |
0 |
0 |
0 |
156 |
| Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
65 |
0 |
0 |
4 |
235 |
| Is distance dying at last? |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
63 |
| Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
50 |
0 |
0 |
0 |
181 |
| Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
62 |
| Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
1 |
104 |
0 |
2 |
8 |
124 |
| Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
0 |
40 |
0 |
2 |
6 |
47 |
| Least Squares Estimation Using Sketched Data with Heteroskedastic Errors |
0 |
0 |
0 |
17 |
0 |
0 |
6 |
29 |
| Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
131 |
| Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
168 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
31 |
1 |
2 |
5 |
120 |
| Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
0 |
0 |
2 |
79 |
| Maximum score estimation of preference parameters for a binary choice model under uncertainty |
0 |
0 |
0 |
48 |
0 |
3 |
22 |
189 |
| Maximum score estimation with nonparametrically generated regressors |
0 |
0 |
0 |
21 |
0 |
0 |
1 |
76 |
| Nonparametric Estimation of an Additive Quantile Regression Model |
0 |
0 |
0 |
362 |
0 |
0 |
2 |
958 |
| Nonparametric Identification of Accelerated Failure Time Competing Risks Models |
0 |
0 |
0 |
41 |
0 |
0 |
0 |
143 |
| Nonparametric Tests of Conditional Treatment Effects |
0 |
0 |
1 |
140 |
0 |
0 |
5 |
480 |
| Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
43 |
0 |
1 |
7 |
104 |
| Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
1 |
0 |
1 |
8 |
42 |
| Nonparametric estimation of an additive quantile regression model |
0 |
0 |
1 |
168 |
0 |
1 |
4 |
475 |
| Nonparametric identification of accelerated failure time competing risks models |
0 |
0 |
0 |
41 |
0 |
0 |
3 |
127 |
| Nonparametric instrumental variables estimation of a quantile regression model |
0 |
0 |
0 |
235 |
0 |
0 |
2 |
662 |
| Nonparametric tests of conditional treatment effects |
0 |
0 |
0 |
31 |
0 |
1 |
4 |
142 |
| Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
54 |
1 |
1 |
3 |
83 |
| Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
30 |
| Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
10 |
0 |
0 |
2 |
31 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
76 |
0 |
0 |
1 |
38 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
40 |
0 |
0 |
0 |
43 |
| Optimal data collection for randomized control trials |
0 |
0 |
0 |
91 |
0 |
0 |
3 |
44 |
| Oracle Estimation of a Change Point in High Dimensional Quantile Regression |
0 |
0 |
0 |
25 |
0 |
0 |
2 |
31 |
| Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
58 |
1 |
6 |
25 |
152 |
| Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
65 |
0 |
0 |
3 |
67 |
| Please call me John: name choice and the assimilation of immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
57 |
1 |
1 |
1 |
124 |
| Property Rights and Fairness: A Tale of Two Koreas |
0 |
0 |
2 |
19 |
0 |
0 |
4 |
47 |
| Recombinant innovation and the boundaries of the firm |
0 |
0 |
0 |
16 |
0 |
0 |
4 |
78 |
| Reform of Unemployment Compensation in Germany: A Nonparametric Bounds Analysis Using Register Data |
0 |
0 |
0 |
30 |
0 |
0 |
1 |
258 |
| Reform of unemployment compensation in Germany: a nonparametric bounds analysis using register data |
0 |
0 |
0 |
83 |
0 |
0 |
1 |
589 |
| SEMIPARAMETRIC ESTIMATION OF A BINARYRESPONSE MODEL WITH A CHANGE-POINTDUE TO A COVARIATE THRESHOLD |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
26 |
| Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects |
0 |
0 |
0 |
508 |
0 |
0 |
2 |
1,327 |
| Semiparametric estimation of a binary response model with a change-point due to a covariate threshold |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
46 |
| Semiparametric estimation of a panel data proportional hazards model with fixed effects |
0 |
0 |
0 |
279 |
0 |
0 |
0 |
793 |
| Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
1 |
1 |
1 |
5 |
2 |
2 |
4 |
45 |
| Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
1 |
1 |
2 |
14 |
1 |
2 |
8 |
57 |
| Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
1 |
16 |
0 |
0 |
2 |
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 |
0 |
7 |
0 |
0 |
2 |
6 |
| Sparse Quantile Regression |
0 |
0 |
2 |
16 |
0 |
0 |
3 |
22 |
| TESTING FOR A GENERAL CLASS OF FUNCTIONAL INEQUALITIES |
0 |
0 |
0 |
54 |
0 |
0 |
1 |
84 |
| TESTING FOR STOCHASTICMONOTONICITY |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
51 |
| Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative |
0 |
0 |
0 |
162 |
0 |
1 |
2 |
560 |
| Testing for a general class of functional inequalities |
0 |
0 |
0 |
20 |
0 |
0 |
1 |
91 |
| Testing for stochastic monotonicity |
0 |
0 |
1 |
2 |
0 |
0 |
2 |
52 |
| Testing for stochastic monotonicity |
0 |
0 |
1 |
53 |
1 |
1 |
4 |
157 |
| Testing for threshold effects in regression models |
0 |
0 |
3 |
207 |
1 |
2 |
7 |
553 |
| Testing functional inequalities |
0 |
0 |
1 |
67 |
0 |
0 |
3 |
134 |
| The identification power of smoothness assumptions in models with counterfactual outcomes |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
84 |
| The lasso for high-dimensional regression with a possible change-point |
0 |
0 |
0 |
33 |
2 |
8 |
34 |
198 |
| Treatment Choice with Nonlinear Regret |
0 |
1 |
4 |
4 |
0 |
1 |
5 |
5 |
| Trends in Quality Adjusted Skill Premia in the US, 1960-2000 |
0 |
0 |
0 |
30 |
0 |
0 |
1 |
93 |
| Trends in Quality-Adjusted Skill Premia in the United States, 1960-2000 |
0 |
0 |
0 |
85 |
0 |
0 |
1 |
194 |
| Trends in quality-adjusted skill premia in the United States, 1960-2000 |
0 |
0 |
0 |
323 |
0 |
1 |
5 |
586 |
| Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
66 |
| Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
71 |
0 |
0 |
2 |
217 |
| Why North Korean Refugees are Reluctant to Compete: The Roles of Cognitive Ability |
1 |
1 |
1 |
13 |
1 |
1 |
2 |
19 |
| Total Working Papers |
8 |
14 |
99 |
7,227 |
31 |
84 |
442 |
20,258 |
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