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Last month |
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A Robust Method for Microforecasting and Estimation of Random Effects |
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21 |
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1 |
4 |
21 |
A Robust Method for Microforecasting and Estimation of Random Effects |
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1 |
16 |
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2 |
14 |
A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent |
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0 |
0 |
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3 |
A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent |
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0 |
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30 |
0 |
0 |
0 |
64 |
A tale of two Koreas: property rights and fairness |
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0 |
0 |
6 |
0 |
1 |
2 |
15 |
Ability, sorting and wage inequality |
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0 |
0 |
173 |
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1 |
3 |
544 |
An Econometric Perspective on Algorithmic Subsampling |
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0 |
1 |
29 |
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0 |
1 |
36 |
An econometric perspective on algorithmic subsampling |
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0 |
0 |
0 |
0 |
0 |
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4 |
Average Adjusted Association: Efficient Estimation with High Dimensional Confounders |
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5 |
0 |
0 |
0 |
9 |
Best Subset Binary Prediction |
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0 |
0 |
1 |
1 |
3 |
4 |
47 |
Best subset binary prediction |
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0 |
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25 |
0 |
2 |
2 |
51 |
Best subset binary prediction |
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0 |
0 |
0 |
0 |
1 |
2 |
5 |
Bounding Treatment Effects by Pooling Limited Information across Observations |
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1 |
1 |
26 |
0 |
1 |
2 |
13 |
Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
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0 |
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1 |
2 |
Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
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0 |
0 |
7 |
0 |
0 |
1 |
35 |
Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
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0 |
0 |
31 |
0 |
2 |
4 |
88 |
Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models |
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0 |
0 |
0 |
0 |
0 |
1 |
2 |
Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions |
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0 |
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22 |
3 |
4 |
5 |
40 |
Causal inference in case-control studies |
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0 |
1 |
1 |
1 |
3 |
19 |
Characterization of the Asymptotic Distribution of Semiparametric M-Estimators |
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176 |
1 |
2 |
3 |
384 |
Characterization of the asymptotic distribution of semiparametric M-estimators |
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0 |
0 |
8 |
1 |
1 |
2 |
43 |
Characterization of the asymptotic distribution of semiparametric M-estimators |
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0 |
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142 |
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0 |
0 |
352 |
DOUBLY ROBUST UNIFORM CONFIDENCE BAND FOR THE CONDITIONAL AVERAGE TREATMENT EFFECT FUNCTION |
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22 |
0 |
2 |
3 |
86 |
Desperate times call for desperate measures: government spending multipliers in hard times |
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0 |
0 |
2 |
0 |
1 |
1 |
9 |
Desperate times call for desperate measures: government spending multipliers in hard times |
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0 |
2 |
1 |
2 |
3 |
24 |
Desperate times call for desperate measures: government spending multipliers in hard times |
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0 |
0 |
7 |
0 |
0 |
0 |
51 |
Desperate times call for desperate measures: government spending multipliers in hard times |
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0 |
1 |
22 |
0 |
0 |
1 |
43 |
Do Institutions Affect Social Preferences? Evidence from Divided Korea |
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0 |
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85 |
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1 |
1 |
180 |
Do institutions affect social preferences? Evidence from divided Korea |
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0 |
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121 |
1 |
2 |
2 |
179 |
Do institutions affect social preferences? Evidence from divided Korea |
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0 |
0 |
0 |
0 |
4 |
4 |
5 |
Does It Matter Who Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited |
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0 |
0 |
33 |
1 |
1 |
1 |
127 |
Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited |
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0 |
0 |
9 |
2 |
3 |
3 |
62 |
Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function |
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0 |
0 |
3 |
1 |
2 |
2 |
22 |
Doubly robust uniform confidence band for the conditional average treatment effect function |
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0 |
0 |
50 |
0 |
0 |
0 |
138 |
Doubly robust uniform confidence band for the conditional average treatment effect function |
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0 |
0 |
0 |
1 |
1 |
3 |
3 |
Doubly robust uniform confidence band for the conditional average treatment effect function |
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0 |
1 |
8 |
0 |
2 |
3 |
39 |
Endogeneity in Quantile Regression Models: A Control Function Approach |
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0 |
0 |
538 |
1 |
1 |
5 |
1,648 |
Endogeneity in quantile regression models: a control function approach |
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0 |
0 |
0 |
1 |
2 |
3 |
9 |
Endogeneity in quantile regression models: a control function approach |
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0 |
0 |
261 |
1 |
1 |
3 |
900 |
Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality |
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0 |
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138 |
0 |
1 |
4 |
316 |
Estimating panel data duration models with censored data |
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0 |
0 |
0 |
1 |
2 |
4 |
6 |
Estimating panel data duration models with censored data |
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0 |
0 |
276 |
0 |
1 |
1 |
814 |
Exact computation of GMM estimators for instrumental variable quantile regression models |
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0 |
1 |
1 |
0 |
2 |
6 |
9 |
Exact computation of GMM estimators for instrumental variable quantile regression models |
0 |
0 |
1 |
25 |
0 |
2 |
5 |
49 |
Factor-Driven Two-Regime Regression |
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0 |
1 |
59 |
0 |
0 |
1 |
110 |
Factor-Driven Two-Regime Regression |
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0 |
0 |
6 |
0 |
0 |
0 |
47 |
Factor-Driven Two-Regime Regression |
0 |
0 |
0 |
12 |
0 |
0 |
3 |
48 |
Fast Inference for Quantile Regression with Tens of Millions of Observations |
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0 |
1 |
14 |
1 |
1 |
3 |
16 |
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling |
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0 |
0 |
19 |
2 |
3 |
4 |
39 |
Filtered and Unfiltered Treatment Effects with Targeting Instruments |
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0 |
0 |
1 |
0 |
2 |
3 |
16 |
Group Shapley Value and Counterfactual Simulations in a Structural Model |
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2 |
12 |
12 |
0 |
2 |
46 |
46 |
High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization |
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0 |
0 |
10 |
0 |
0 |
0 |
32 |
Identification of a competing risks model with unknown transformations of latent failure times |
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0 |
0 |
97 |
0 |
1 |
2 |
401 |
Identifying Effects of Multivalued Treatments |
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0 |
0 |
45 |
0 |
2 |
2 |
68 |
Identifying Effects of Multivalued Treatments |
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0 |
1 |
43 |
0 |
1 |
2 |
38 |
Identifying effects of multivalued treatments |
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0 |
0 |
1 |
0 |
2 |
4 |
6 |
Identifying effects of multivalued treatments |
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0 |
0 |
10 |
0 |
0 |
1 |
26 |
Identifying effects of multivalued treatments |
0 |
0 |
0 |
5 |
0 |
1 |
1 |
32 |
Identifying the Effect of Persuasion |
1 |
1 |
1 |
13 |
2 |
2 |
2 |
40 |
Identifying the effect of persuasion |
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0 |
0 |
2 |
0 |
0 |
0 |
11 |
Identifying the effect of persuasion |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
7 |
Identifying the effect of persuasion |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
39 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
24 |
0 |
0 |
0 |
113 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
64 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
Implementing intersection bounds in Stata |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
5 |
Implicit Bias against a Capitalistic Society Predicts Market Earnings |
0 |
0 |
2 |
6 |
0 |
0 |
4 |
7 |
Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds |
0 |
0 |
1 |
30 |
1 |
3 |
6 |
12 |
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic |
0 |
0 |
0 |
46 |
1 |
5 |
8 |
38 |
Inference in a class of optimization problems: Con?dence regions and ?nite sample bounds on errors in coverage probabilities |
0 |
0 |
0 |
10 |
1 |
2 |
4 |
10 |
Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities |
0 |
0 |
0 |
17 |
1 |
1 |
2 |
21 |
Institutions, Competitiveness and Cognitive Ability |
0 |
0 |
0 |
34 |
0 |
0 |
2 |
91 |
Institutions, competitiveness and cognitive ability |
0 |
0 |
2 |
10 |
0 |
0 |
2 |
10 |
International trends in technological progress: stylized facts from patent citations, 1980-2011 |
0 |
0 |
0 |
66 |
1 |
1 |
1 |
89 |
International trends in technological progress: stylized facts from patent citations, 1980-2011 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
Intersection Bounds: estimation and inference |
0 |
0 |
1 |
88 |
0 |
0 |
3 |
328 |
Intersection Bounds: estimation and inference |
0 |
0 |
1 |
1 |
0 |
0 |
2 |
5 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
36 |
1 |
1 |
3 |
126 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
17 |
0 |
0 |
1 |
96 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
Intersection bounds: estimation and inference |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
Is Distance Dying at Last? |
0 |
0 |
1 |
2 |
0 |
1 |
6 |
37 |
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? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
66 |
0 |
1 |
1 |
239 |
Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations |
0 |
0 |
0 |
34 |
1 |
1 |
3 |
159 |
Is distance dying at last? |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
64 |
Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
51 |
1 |
1 |
1 |
184 |
Is distance dying at last? Falling home bias in fixed effects models of patent citations |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
64 |
Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
0 |
106 |
1 |
1 |
1 |
133 |
Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
0 |
41 |
0 |
1 |
1 |
53 |
Knowledge spillovers and patent citations: trends in geographic localization, 1976-2015 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
3 |
Learning the Effect of Persuasion via Difference-In-Differences |
0 |
1 |
10 |
10 |
1 |
7 |
37 |
37 |
Least Squares Estimation Using Sketched Data with Heteroskedastic Errors |
0 |
0 |
0 |
20 |
1 |
1 |
2 |
37 |
Local Identification of Nonparametric and Semiparametric Models |
0 |
0 |
0 |
49 |
1 |
1 |
2 |
173 |
Local Identification of Nonparametric and Semiparametric Models |
0 |
1 |
1 |
13 |
0 |
2 |
4 |
136 |
Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
80 |
Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
31 |
1 |
2 |
13 |
136 |
Local identification of nonparametric and semiparametric models |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
Maximum score estimation of preference parameters for a binary choice model under uncertainty |
1 |
1 |
1 |
1 |
1 |
2 |
3 |
4 |
Maximum score estimation of preference parameters for a binary choice model under uncertainty |
0 |
0 |
0 |
48 |
0 |
1 |
2 |
214 |
Maximum score estimation with nonparametrically generated regressors |
0 |
0 |
0 |
1 |
0 |
2 |
3 |
7 |
Maximum score estimation with nonparametrically generated regressors |
0 |
0 |
0 |
21 |
0 |
1 |
3 |
81 |
Nonparametric Estimation of an Additive Quantile Regression Model |
0 |
0 |
0 |
362 |
0 |
2 |
4 |
966 |
Nonparametric Identification of Accelerated Failure Time Competing Risks Models |
0 |
0 |
0 |
42 |
5 |
6 |
8 |
153 |
Nonparametric Tests of Conditional Treatment Effects |
0 |
0 |
0 |
141 |
1 |
2 |
2 |
488 |
Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
46 |
Nonparametric estimation and inference under shape restrictions |
0 |
0 |
0 |
43 |
0 |
1 |
1 |
110 |
Nonparametric estimation of an additive quantile regression model |
0 |
0 |
0 |
168 |
0 |
2 |
4 |
481 |
Nonparametric estimation of an additive quantile regression model |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
4 |
Nonparametric identification of accelerated failure time competing risks models |
0 |
0 |
0 |
41 |
1 |
1 |
1 |
129 |
Nonparametric instrumental variables estimation of a quantile regression model |
0 |
0 |
0 |
235 |
0 |
1 |
1 |
664 |
Nonparametric tests of conditional treatment effects |
0 |
0 |
0 |
31 |
1 |
2 |
3 |
145 |
Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
11 |
0 |
0 |
1 |
33 |
Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
39 |
0 |
1 |
2 |
34 |
Optimal Data Collection for Randomized Control Trials |
0 |
0 |
0 |
54 |
0 |
1 |
1 |
86 |
Optimal data collection for randomized control trials |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Optimal data collection for randomized control trials |
0 |
0 |
0 |
40 |
1 |
1 |
1 |
46 |
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 |
76 |
0 |
0 |
0 |
38 |
Optimal data collection for randomized control trials |
0 |
0 |
0 |
1 |
0 |
1 |
2 |
3 |
Optimal data collection for randomized control trials |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
Oracle Estimation of a Change Point in High Dimensional Quantile Regression |
0 |
0 |
0 |
25 |
0 |
0 |
1 |
35 |
Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
1 |
1 |
1 |
59 |
1 |
2 |
3 |
166 |
Please Call Me John: Name Choice and the Assimilation of Immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
66 |
1 |
2 |
4 |
75 |
Please call me John: name choice and the assimilation of immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
0 |
1 |
2 |
4 |
7 |
Please call me John: name choice and the assimilation of immigrants in the United States, 1900-1930 |
0 |
0 |
0 |
57 |
1 |
3 |
5 |
130 |
Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors |
0 |
0 |
1 |
11 |
0 |
0 |
3 |
13 |
Property Rights and Fairness: A Tale of Two Koreas |
0 |
0 |
0 |
20 |
0 |
1 |
2 |
51 |
Recombinant innovation and the boundaries of the firm |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
Recombinant innovation and the boundaries of the firm |
0 |
0 |
0 |
16 |
1 |
1 |
1 |
80 |
Reform of Unemployment Compensation in Germany: A Nonparametric Bounds Analysis Using Register Data |
0 |
0 |
0 |
30 |
0 |
0 |
0 |
260 |
Reform of unemployment compensation in Germany: a nonparametric bounds analysis using register data |
0 |
0 |
0 |
83 |
0 |
1 |
1 |
590 |
SEMIPARAMETRIC ESTIMATION OF A BINARYRESPONSE MODEL WITH A CHANGE-POINTDUE TO A COVARIATE THRESHOLD |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
28 |
SGMM: Stochastic Approximation to Generalized Method of Moments |
0 |
0 |
0 |
24 |
2 |
5 |
19 |
35 |
Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects |
0 |
0 |
0 |
508 |
0 |
1 |
1 |
1,330 |
Semiparametric estimation of a binary response model with a change-point due to a covariate threshold |
0 |
0 |
0 |
2 |
0 |
1 |
1 |
48 |
Semiparametric estimation of a panel data proportional hazards model with fixed effects |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
Semiparametric estimation of a panel data proportional hazards model with fixed effects |
0 |
0 |
0 |
279 |
0 |
1 |
1 |
795 |
Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
1 |
15 |
0 |
0 |
2 |
62 |
Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
42 |
Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate |
0 |
0 |
0 |
5 |
0 |
1 |
2 |
51 |
Sparse HP filter: Finding kinks in the COVID-19 contact rate |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
8 |
Sparse Quantile Regression |
0 |
0 |
1 |
9 |
0 |
1 |
3 |
13 |
Sparse Quantile Regression |
0 |
0 |
0 |
17 |
0 |
0 |
2 |
28 |
TESTING FOR A GENERAL CLASS OF FUNCTIONAL INEQUALITIES |
0 |
0 |
0 |
54 |
1 |
3 |
3 |
87 |
TESTING FOR STOCHASTICMONOTONICITY |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
52 |
Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative |
0 |
0 |
0 |
162 |
0 |
1 |
3 |
563 |
Testing for a general class of functional inequalities |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
2 |
Testing for a general class of functional inequalities |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
93 |
Testing for stochastic monotonicity |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
53 |
Testing for stochastic monotonicity |
0 |
0 |
0 |
53 |
0 |
0 |
0 |
157 |
Testing for threshold effects in regression models |
0 |
0 |
2 |
209 |
1 |
3 |
8 |
562 |
Testing functional inequalities |
0 |
0 |
0 |
67 |
1 |
1 |
1 |
135 |
The ET Interview: Professor Joel L. Horowitz |
0 |
20 |
20 |
20 |
1 |
25 |
27 |
27 |
The identification power of smoothness assumptions in models with counterfactual outcomes |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
2 |
The identification power of smoothness assumptions in models with counterfactual outcomes |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
87 |
The lasso for high-dimensional regression with a possible change-point |
0 |
0 |
0 |
33 |
1 |
1 |
4 |
227 |
The lasso for high-dimensional regression with a possible change-point |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Treatment Choice with Nonlinear Regret |
0 |
0 |
1 |
7 |
0 |
1 |
2 |
13 |
Treatment Choice, Mean Square Regret and Partial Identification |
0 |
0 |
1 |
9 |
0 |
1 |
2 |
10 |
Treatment Effects with Targeting Instruments |
0 |
0 |
2 |
5 |
1 |
3 |
5 |
18 |
Trends in Quality Adjusted Skill Premia in the US, 1960-2000 |
0 |
0 |
0 |
30 |
1 |
2 |
2 |
95 |
Trends in Quality-Adjusted Skill Premia in the United States, 1960-2000 |
0 |
0 |
0 |
85 |
2 |
4 |
4 |
200 |
Trends in quality-adjusted skill premia in the United States, 1960-2000 |
0 |
0 |
0 |
323 |
1 |
3 |
4 |
590 |
Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
68 |
Uniform confidence bands for functions estimated nonparametrically with instrumental variables |
0 |
0 |
0 |
72 |
0 |
0 |
0 |
219 |
Why North Korean Refugees are Reluctant to Compete: The Roles of Cognitive Ability |
0 |
0 |
2 |
16 |
1 |
2 |
5 |
25 |
Total Working Papers |
3 |
28 |
76 |
7,492 |
67 |
215 |
477 |
21,252 |