J
James L. Powell
Researcher at University of California, Berkeley
Publications - 74
Citations - 11586
James L. Powell is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 34, co-authored 72 publications receiving 10848 citations. Previous affiliations of James L. Powell include Massachusetts Institute of Technology & University of Wisconsin-Madison.
Papers
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Least absolute deviations estimation for the censored regression model
TL;DR: In this paper, an alternative to maximum likelihood estimation of the parameters of the censored regression (or censored 'Tobit' model) is proposed, which is a generalization of least absolute deviations estimation for the standard linear model, and is also robust to heteroscedasticity.
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Semiparametric Estimation of Index Coefficients
TL;DR: In this paper, the density-weighted average derivative of a general regression function is estimated using nonparametric kernel estimators of the density of the regressors, based on sample analogues of the product moment representation of the average derivative.
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Asymmetric least squares estimation and testing
Whitney K. Newey,James L. Powell +1 more
TL;DR: In this article, the authors consider estimation and hypothesis tests for coefficients of linear regression models, where the coefficient estimates are based on location measures defined by an asymmetric least squares criterion function.
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Instrumental variable estimation of nonparametric models
Whitney K. Newey,James L. Powell +1 more
TL;DR: In this article, the identification and estimation of structural functions for nonparametric conditional moment restrictions are given. But they do not provide sufficient identification conditions for exponential families and discrete variables.
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Censored regression quantiles
TL;DR: In this article, the form of the conditional quantiles for the censored regression models is heuristically derived and discussed, and the resulting estimators of the regression coefficients, which include the censored LAD estimator as a special case, are shown to be consistent and asymptotically normally distributed under appropriately translated versions of the corresponding assumptions for the former approach.