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Full-Text Articles in Social and Behavioral Sciences
On Bartlett Correctability Of Empirical Likelihood In Generalized Power Divergence Family, Lorenzo Camponovo, Taisuke Otsu
On Bartlett Correctability Of Empirical Likelihood In Generalized Power Divergence Family, Lorenzo Camponovo, Taisuke Otsu
Cowles Foundation Discussion Papers
Baggerly (1998) showed that empirical likelihood is the only member in the Cressie-Read power divergence family to be Bartlett correctable. This paper strengthens Baggerly’s result by showing that in a generalized class of the power divergence family, which includes the Cressie-Read family and other nonparametric likelihood such as Schennach’s (2005, 2007) exponentially tilted empirical likelihood, empirical likelihood is still the only member to be Bartlett correctable.
Empirical Likelihood For Regression Discontinuity Design, Taisuke Otsu, Ke-Li Xu
Empirical Likelihood For Regression Discontinuity Design, Taisuke Otsu, Ke-Li Xu
Cowles Foundation Discussion Papers
This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity designs and treat the regression functions as nonparametric. The proposed inference procedures do not require asymptotic variance estimation and the confidence sets have natural shapes, unlike the conventional Wald-type method. These features are illustrated by simulations and an empirical example which evaluates the effect of class size on pupils’ scholastic achievements. Bandwidth selection methods, higher-order properties, and extensions to incorporate additional covariates and parametric functional forms are also discussed.
Empirical Likelihood For Nonparametric Additive Models, Taisuke Otsu
Empirical Likelihood For Nonparametric Additive Models, Taisuke Otsu
Cowles Foundation Discussion Papers
Nonparametric additive modeling is a fundamental tool for statistical data analysis which allows flexible functional forms for conditional mean or quantile functions but avoids the curse of dimensionality for fully nonparametric methods induced by high-dimensional covariates. This paper proposes empirical likelihood-based inference methods for unknown functions in three types of nonparametric additive models: (i) additive mean regression with the identity link function, (ii) generalized additive mean regression with a known non-identity link function, and (iii) additive quantile regression. The proposed empirical likelihood ratio statistics for the unknown functions are asymptotically pivotal and converge to chi-square distributions, and their associated confidence …
Moderate Deviations Of Generalized Method Of Moments And Empirical Likelihood Estimators, Taisuke Otsu
Moderate Deviations Of Generalized Method Of Moments And Empirical Likelihood Estimators, Taisuke Otsu
Cowles Foundation Discussion Papers
This paper studies moderate deviation behaviors of the generalized method of moments and generalized empirical likelihood estimators for generalized estimating equations, where the number of equations can be larger than the number of unknown parameters. We consider two cases for the data generating probability measure: the model assumption and local contaminations or deviations from the model assumption. For both cases, we characterize the first-order terms of the moderate deviation error probabilities of these estimators. Our moderate deviation analysis complements the existing literature of the local asymptotic analysis and misspecification analysis for estimating equations, and is useful to evaluate power and …