Errors-in-Variables Estimation with No Instruments
This paper develops 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 and consistent 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
|Date of creation:||Jan 2009|
|Date of revision:||Jan 2009|
|Contact details of provider:|| Postal: |
Web page: http://www.rcfea.org
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:rim:rimwps:30_09. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco Savioli)
If references are entirely missing, you can add them using this form.