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Errors-in-Variables Estimation with No Instruments

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Author Info

  • Ramazan Gencay

    ()
    ( Department of Economics, Simon Fraser University)

  • Nikola Gradojevic

    ()
    ( Faculty of Business Administration, Lakehead University)

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

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File URL: http://www.rcfea.org/RePEc/pdf/wp30_09.pdf
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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 30_09.

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Date of creation: Jan 2009
Date of revision: Jan 2009
Handle: RePEc:rim:rimwps:30_09

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Related research

Keywords: Cointegration; discrete wavelet transformation; maximum overlap wavelet transformation; energy decomposition; errors-in-variables; persistence;

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Cited by:
  1. Reese, Simon & Li, Yushu, 2013. "Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements," Working Papers 2013:36, Lund University, Department of Economics.

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