| Title:
|
Efficient robust estimation of time-series regression models (English) |
| Author:
|
Čížek, Pavel |
| Language:
|
English |
| Journal:
|
Applications of Mathematics |
| ISSN:
|
0862-7940 (print) |
| ISSN:
|
1572-9109 (online) |
| Volume:
|
53 |
| Issue:
|
3 |
| Year:
|
2008 |
| Pages:
|
267-279 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
The paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust fit. Just like many existing two-step robust methods, the proposed 2S-LWS estimator preserves robust properties of the initial robust estimate. However, contrary to the existing methods, the first-order asymptotic behavior of 2S-LWS is fully independent of the initial estimate under mild conditions. We propose data-adaptive weighting schemes that perform well both in the cross-section and time-series data and prove the asymptotic normality and efficiency of the resulting procedure. A simulation study documents these theoretical properties in finite samples. (English) |
| Keyword:
|
asymptotic efficiency |
| Keyword:
|
least weighted squares |
| Keyword:
|
robust regression |
| Keyword:
|
time series |
| MSC:
|
62F10 |
| MSC:
|
62F12 |
| MSC:
|
62F35 |
| MSC:
|
62J05 |
| MSC:
|
62L12 |
| MSC:
|
62M10 |
| MSC:
|
65C60 |
| idZBL:
|
Zbl 1189.62140 |
| idMR:
|
MR2411129 |
| DOI:
|
10.1007/s10492-008-0009-x |
| . |
| Date available:
|
2010-07-20T12:23:04Z |
| Last updated:
|
2020-07-02 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/140320 |
| . |
| Reference:
|
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| Reference:
|
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| Reference:
|
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| Reference:
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| . |
