Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Errors-in-Variables Estimation with Wavelets |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
|
| Journal Title | Journal of Statistical Computation and Simulation |
| Publisher | Taylor & Francis |
| Geographical Reach | international |
| ISSN | 0094-9655 |
| Volume | 81 |
| Number | 11 |
| Page Range | 1545 - 1564 |
| Date | 2011 |
| Abstract Text | This paper proposes a wavelet (spectral) approach to estimate the parameters of a linear regression model where the regressand and the regressors are persistent processes and contain a measurement error. We propose a wavelet filtering approach which does not require instruments and yields unbiased estimates for the intercept and the slope parameters. Our Monte Carlo results also show that the wavelet approach is particularly effective when measurement errors for the regressand and the regressor are serially correlated. With this paper, we hope to bring a fresh perspective and stimulate further theoretical research in this area. |
| Free access at | Official URL |
| Digital Object Identifier | 10.1080/00949655.2010.495073 |
| Other Identification Number | merlin-id:5961 |
| PDF File |
Download from ZORA
|
| Export |
BibTeX
EP3 XML (ZORA)
|