Errors-in-Variables Estimation with Wavelets
Simon Fraser University
IÉSEG School of Management; University of Bologna - Rimini Center for Economic Analysis (RCEA)
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.
Number of Pages in PDF File: 24
Keywords: Cointegration, discrete wavelet transformation, maximum overlap wavelet transformation, energy decomposition, errors-in-variables, persistence
JEL Classification: C1, C2, C12, C22, F31, G0, G1working papers series
Date posted: June 17, 2009 ; Last revised: November 20, 2009
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