Errors-in-Variables Estimation with Wavelets
24 Pages Posted: 17 Jun 2009 Last revised: 20 Nov 2009
Date Written: October 2009
Abstract
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.
Keywords: Cointegration, discrete wavelet transformation, maximum overlap wavelet transformation, energy decomposition, errors-in-variables, persistence
JEL Classification: C1, C2, C12, C22, F31, G0, G1
Suggested Citation: Suggested Citation
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