@article{0e8f9244277a442c999dd7a97f232dac,
title = "Nearly weighted risk minimal unbiased estimation",
abstract = "Consider a small-sample parametric estimation problem, such as the estimation of the coefficient in a Gaussian AR(1). We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We also apply our generic approach to the median unbiased estimation of the degree of time variation in a Gaussian local-level model, and to a quantile unbiased point forecast for a Gaussian AR(1) process.",
keywords = "Autoregression, Mean bias, Median bias, Quantile unbiased forecast",
author = "M{\"u}ller, {Ulrich K.} and Yulong Wang",
note = "Funding Information: The authors thank seminar participants at Harvard/MIT, Princeton, University of Kansas and University of Pennsylvania, an associate editor and two referees for helpful comments and suggestions. M?ller gratefully acknowledges financial support by the NSF via grant SES-1226464. Funding Information: The authors thank seminar participants at Harvard/MIT, Princeton, University of Kansas and University of Pennsylvania, an associate editor and two referees for helpful comments and suggestions. M{\"u}ller gratefully acknowledges financial support by the NSF via grant SES-1226464. Publisher Copyright: {\textcopyright} 2018 Elsevier B.V.",
year = "2019",
month = mar,
doi = "10.1016/j.jeconom.2018.11.016",
language = "English (US)",
volume = "209",
pages = "18--34",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "1",
}