Nearly weighted risk minimal unbiased estimation

Ulrich K. Müller, Yulong Wang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)18-34
Number of pages17
JournalJournal of Econometrics
Issue number1
StatePublished - Mar 2019

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


  • Autoregression
  • Mean bias
  • Median bias
  • Quantile unbiased forecast


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