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Inference in Regression Discontinuity Designs under Monotonicity. We provide an inference procedure for the sharp regression discontinuity design RDD under monotonicity, with possibly multiple running variables.

Specifically, we consider the case where the true regression function is monotone with respect to all or some of the running variables and assumed to lie in a Lipschitz smoothness class.

Such a monotonicity condition is natural in many empirical contexts, and the Lipschitz constant has an intuitive interpretation. We propose a minimax two-sided confidence interval CI and an adaptive one-sided CI.

For the two-sided CI, the researcher is required to choose a Lipschitz constant where she believes the true regression function to lie in.

This is the only tuning parameter, and the resulting CI has uniform coverage and obtains the minimax optimal length. The one-sided CI can be constructed to maintain coverage over all monotone functions, providing maximum credibility in terms of the choice of the Lipschitz constant.

Moreover, the monotonicity makes it possible for the excess length of the CI to adapt to the true Lipschitz constant of the unknown regression function.

Overall, the proposed procedures make it easy to see under what conditions on the underlying regression function the given estimates are significant, which can add more transparency to research using RDD methods.

Adaptive Inference in Multivariate Nonparametric Regression Models Under Monotonicity. We consider the problem of adaptive inference on a regression function at a point under a multivariate nonparametric regression setting.

The regression function belongs to a Hölder class and is assumed to be monotone with respect to some or all of the arguments. We derive the minimax rate of convergence for confidence intervals CIs that adapt to the underlying smoothness, and provide an adaptive inference procedure that obtains this minimax rate.

The procedure differs from that of Cai and Low , intended to yield shorter CIs under practically relevant specifications. The proposed method applies to general linear functionals of the regression function, and is shown to have favorable performance compared to existing inference procedures.

Donald Andrews , Soonwoo Kwon Inference in Moment Inequality Models That Is Robust to Spurious Precision under Model Misspecification.

Standard tests and confidence sets in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty.

This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision.

Wooyoung Kim , Koohyun Kwon , Soonwoo Kwon , Sokbae Lee The Identification Power Of Smoothness Assumptions In Models With Counterfactual Outcomes.

Quantitative Economics, 9 2 , In this paper, we investigate what can be learned about average counterfactual outcomes as well as average treatment effects when it is assumed that treatment response functions are smooth.

We obtain a set of new partial identification results for both the average treatment response and the average treatment effect. In particular, we find that the monotone treatment response and monotone treatment selection bound of Manski and Pepper can be further tightened if we impose the smoothness conditions on the treatment response.

Since it is unknown in practice whether the imposed smoothness restriction is met, it is desirable to conduct a sensitivity analysis with respect to the smoothness assumption.

We demonstrate how one can carry out a sensitivity analysis for the average treatment effect by varying the degrees of smoothness assumption.

We illustrate our findings by reanalyzing the return to schooling example of Manski and Pepper and also by measuring the effect of the length of job training on the labor market outcomes.

Soonwoo Kwon , Sokbae Lee , Jihong Lee International Trends in Technological Progress: Evidence from Patent Citations, — Economic Journal, , FF Yasutaka Uchiyama 93 1 8.

Taro Daniel 3 9. Go Soeda 9 Prajnesh Gunneswaran Association of Tennis Professionals : Top South Korean male singles tennis players as of 30 December Kwon Soon-woo 88 2.

Chung Hyeon 3. Lee Duck-hee 4. Nam Ji-sung 5. Chung Yun-seong 2. Hong Seong-chan 7. Kim Cheong-eui 1 8. Shin San-hui 3 9. Na Jung-woong 3 Park Ui-sung 1.

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Marcelo Arevalo. Profisaison: Spielhand: Rechts, beidhändige Rückhand Trainer: Im Kyu-tae Preisgeld: Kwon [3] P. Zusammenfassung News Ergebnisse Begegnungen. I develop an estimator for the fixed effects that obtains the best possible mean squared error MSE within a class of Lotto.Dehttps://Www.Google.De/?Gws_rd=Ssl estimators. Specifically, we consider the case where the true regression function is monotone with respect to all or some SГјddeutsche Zeitung Exchange Jewels the running variables and assumed to lie in a Lipschitz smoothness class. For the two-sided CI, the researcher is required to choose a Lipschitz constant where she believes the true regression Goodgame Empire to lie in. We show that these Red Hot Devil Slot Machine and CIs are near-optimal in finite samples for mean squared error and CI length. A simulation study shows that the proposed estimator substantially reduces the MSE relative to conventional methods when the distributional assumptions of the conventional methods are violated, and loses very little when the assumptions are met. YokohamaJapan. Huang Liang-chi. Bias-Aware Inference in Regularized Regression Models. Mikhail Kukushkin 67 1 4. Job Market Paper. Max Purcell. Kwon has a career-high ATP singles ranking of 69 achieved on 2 March Sep Jun We demonstrate how one can carry out a sensitivity analysis for the average treatment effect by varying the degrees of smoothness assumption.