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Alternative errors-in-variables models and their applications in finance research

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  • Chen, Hong-Yi
  • Lee, Alice C.
  • Lee, Cheng-Few

Abstract

Specification error and measurement error are two major issues in finance research. The main purpose of this paper is (i) to review and extend existing errors-in-variables (EIV) estimation methods, including classical method, grouping method, instrumental variable method, mathematical programming method, maximum likelihood method, LISREL method, and the Bayesian approach; (ii) to investigate how EIV estimation methods have been used to finance related studies, such as cost of capital, capital structure, investment equation, and test capital asset pricing models; and (iii) to give a more detailed explanation of the methods used by Almeida et al. (2010).

Suggested Citation

  • Chen, Hong-Yi & Lee, Alice C. & Lee, Cheng-Few, 2015. "Alternative errors-in-variables models and their applications in finance research," The Quarterly Review of Economics and Finance, Elsevier, vol. 58(C), pages 213-227.
  • Handle: RePEc:eee:quaeco:v:58:y:2015:i:c:p:213-227
    DOI: 10.1016/j.qref.2014.12.002
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    Cited by:

    1. Milan Hladík & Michal Černý & Jaromír Antoch, 2020. "EIV regression with bounded errors in data: total ‘least squares’ with Chebyshev norm," Statistical Papers, Springer, vol. 61(1), pages 279-301, February.
    2. Cheng-Few Lee & Chengru Hu & Maggie Foley, 2021. "Differential risk effect of inside debt, CEO compensation diversification, and firm investment," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 505-543, February.

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    More about this item

    Keywords

    Measurement error; Errors-in-variables; Cost of capital; Capital structure; Investment equation; Capital asset pricing model;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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