Motivated by Manski and Tamer (2002) and especially their partial identification analysis of the regression model where …

We reconsider the partial identification analysis of the regression model in Manski and Tamer (2002) where one …

Motivated by Manski and Tamer (2002) and especially their partial identification analysis of the regression model where …

We present a fundamentally unique method of nonparametric regression using clusters and test it against classically … established methods. We compare two nonlinear regression estimation packages called ‘NNS', Viole (NNS: nonlinear nonparametric … DT models, while finding an advantage to NNS in ST and large sample cases. Regression coefficients are sometimes regarded …

summarizing "big data" in regression settings, and it is often used for informal testing of substantive hypotheses such as … linearity or monotonicity of the regression function. This paper presents a foundational, thorough analysis of binscatter: We … regression function, and several other new methods, applicable to canonical binscatter as well as higher-order polynomial …

models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high …

order to model income, we apply distributional regression relating potentially each parameter of the conditional income …-term panel data from Germany, we build both mean and distributional regression models with the established 0.5 probability cutoff … income regression model into the distributional regression framework does not improve predictions further but has the …

the semiparametric efficiency bound for the quantile regression parameter β ( τ ) in Koenker and Bassett (1978). The first … implies that the Koenker–Bassett estimator semiparametrically efficiently estimates the quantile regression parameter that … produces parsimonious descriptive statistics for the conditional distribution. Therefore, quantile regression shares the …