Abstract
We consider the empirical likelihood method for estimation of distribution and quantile functions where side information is incorporated through moment conditions. We systematically study the asymptotic properties of the estimators, such as the uniform strong laws of large numbers and weak convergence over classes of functions. Two Monte Carlo examples are also given to illustrate the practical utility of the method. © 2013 Elsevier B.V. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 613-623 |
| Journal | Journal of Econometrics |
| Volume | 178 |
| Issue number | PART 3 |
| DOIs | |
| Publication status | Published - Jan 2014 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Empirical likelihood
- Quantile estimation
- Uniform CLT
- Uniform SLLN
Policy Impact
- Cited in Policy Documents
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