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.
Original language | English (US) |
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Pages (from-to) | 18-34 |
Number of pages | 17 |
Journal | Journal of Econometrics |
Volume | 209 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2019 |
All Science Journal Classification (ASJC) codes
- Economics and Econometrics
Keywords
- Autoregression
- Mean bias
- Median bias
- Quantile unbiased forecast