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Olive: a simple method for estimating betas when factors are measured with error

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  • Meng, Ginger
  • Hu, Gang
  • Bai, Jushan

Abstract

We propose a simple and intuitive method for estimating betas when factors are measured with error: ordinary least squares instrumental variable estimator (OLIVE). OLIVE performs well when the number of instruments becomes large, while the performance of conventional instrumental variable methods becomes poor or even infeasible. In an empirical application, OLIVE beta estimates improve R-squared significantly. More importantly, our results help resolve two puzzling findings in the prior literature: first, the sign of average risk premium on the beta for market return changes from negative to positive; second, the estimated value of average zero-beta rate is no longer too high.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 33183.

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Date of creation: Mar 2007
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Publication status: Published in The Journal of Financial Research 1.XXXIV(2011): pp. 27-60
Handle: RePEc:pra:mprapa:33183

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Keywords: betas; factor analysis; GMM; FIML; measurement error;

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Cited by:
  1. Prono, Todd, 2011. "When A Factor Is Measured with Error: The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Factor Models," MPRA Paper 33593, University Library of Munich, Germany.
  2. Jushan Bai & Shuzhong Shi, 2011. "Estimating High Dimensional Covariance Matrices and its Applications," Annals of Economics and Finance, Society for AEF, vol. 12(2), pages 199-215, November.

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