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Measurement Errors in Investment Equations

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  • Heitor Almeida
  • Murillo Campello
  • Antonio F. Galvao
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    Abstract

    We use Monte Carlo simulations and real data to assess the performance of methods dealing with measurement error in investment equations. Our experiments show that fixed effects, error heteroscedasticity, and data skewness severely affect the performance and reliability of methods found in the literature. Estimators that use higher-order moments return biased coefficients for (both) mismeasured and perfectly measured regressors. These estimators are also very inefficient. Instrumental-variable-type estimators are more robust and efficient, although they require restrictive assumptions. We estimate empirical investment models using alternative methods. Real-world investment data contain firm-fixed effects and heteroscedasticity, causing high-order moments estimators to deliver coefficients that are unstable and not economically meaningful. Instrumental variables methods yield estimates that are robust and conform to theoretical priors. Our analysis provides guidance for dealing with measurement errors under circumstances researchers are likely to find in practice. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

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    File URL: http://hdl.handle.net/10.1093/rfs/hhq058
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    Bibliographic Info

    Article provided by Society for Financial Studies in its journal Review of Financial Studies.

    Volume (Year): 23 (2010)
    Issue (Month): 9 ()
    Pages: 3279-3328

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    Handle: RePEc:oup:rfinst:v:23:y:2010:i:9:p:3279-3328

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    Cited by:
    1. Galvao, Antonio F. & Montes-Rojas, Gabriel & Sosa-Escudero, Walter & Wang, Liang, 2013. "Tests for skewness and kurtosis in the one-way error component model," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 35-52.
    2. Brown, James R. & Floros, Ioannis V., 2012. "Access to private equity and real firm activity: Evidence from PIPEs," Journal of Corporate Finance, Elsevier, vol. 18(1), pages 151-165.
    3. Timothy Erickson & Toni M. Whited, 2012. "Treating Measurement Error in Tobin's q," Review of Financial Studies, Society for Financial Studies, vol. 25(4), pages 1286-1329.
    4. Jacob, Marcus & Jacob, Martin, 2013. "Taxation and the cash flow sensitivity of dividends," Economics Letters, Elsevier, vol. 118(1), pages 186-188.
    5. Behr, Patrick & Norden, Lars & Noth, Felix, 2013. "Financial constraints of private firms and bank lending behavior," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3472-3485.
    6. Bo Becker & Marcus Jacob & Martin Jacob, 2011. "Payout Taxes and the Allocation of Investment," NBER Working Papers 17481, National Bureau of Economic Research, Inc.
    7. Badertscher, Brad & Shroff, Nemit & White, Hal D., 2013. "Externalities of public firm presence: Evidence from private firms' investment decisions," Journal of Financial Economics, Elsevier, vol. 109(3), pages 682-706.
    8. Borisova, Ginka & Brown, James R., 2013. "R&D sensitivity to asset sale proceeds: New evidence on financing constraints and intangible investment," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 159-173.
    9. Brown, James R. & Martinsson, Gustav & Petersen, Bruce C., 2012. "Do financing constraints matter for R&D?," European Economic Review, Elsevier, vol. 56(8), pages 1512-1529.
    10. Firth, Michael & Malatesta, Paul H. & Xin, Qingquan & Xu, Liping, 2012. "Corporate investment, government control, and financing channels: Evidence from China's Listed Companies," Journal of Corporate Finance, Elsevier, vol. 18(3), pages 433-450.

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