More efficient estimation under non-normality when higher moments do not depend on the regressors, using residual augmented least squares

DOI10.1016/J.JECONOM.2008.01.003zbMath1418.62476OpenAlexW1964182009MaRDI QIDQ292153

F. Blanchet-Sadri, M. Dambrine

Publication date: 10 June 2016

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2008.01.003

zbMATH Keywords

efficiencynon-normality

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Linear regression; mixed models (62J05) Point estimation (62F10)

Related Items (7)

THE ET INTERVIEW: PROFESSOR PETER SCHMIDT ⋮ Transformed regression-based long-horizon predictability tests ⋮ More powerful cointegration tests with non-normal errors ⋮ RALS-LM unit root test with trend breaks and non-normal errors: application to the Prebisch-Singer hypothesis ⋮ Testing for stationarity with covariates: more powerful tests with non-normal errors ⋮ Improved autoregressive forecasts in the presence of non-normal errors ⋮ Residual-augmented IVX predictive regression

Cites Work

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• Adaptive estimation of regression models via moment restrictions
• Redundancy of moment conditions
• Improved instrumental variables and generalized method of moments estimators
• Asymptotic efficiency in estimation with conditional moment restrictions
• Some Properties of Beta and Gamma Distributions

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