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November 16, 2011Review of Financial Studies263 citations

Treating Measurement Error in Tobin'sq

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TETimothy B. EricksonTWToni M. Whited

Key Points

  • This research aims to evaluate different approaches to correcting measurement errors in investment regressions.
  • Compared high-order moment estimators, dynamic panel estimators, and instrumental variables estimators.
  • Developed a minimum distance technique for applying high-order moment estimators to unbalanced panel data.
  • Examined the performance of estimators under both correct and misspecified models.
  • All estimators can perform well with correct specification and show bias when misspecified.
  • Misspecification is most readily detectable using high-order moment estimators.
  • The new technique effectively extends the application of high-order moment estimators.

Abstract

We compare the ability of three measurement error remedies to deliver unbiased estimates of coefficients in investment regressions. We examine high-order moment estimators, dynamic panel estimators, and simple instrumental variables estimators that use lagged mismeasured regressors as instruments. We show that recent investigations of this question are largely uninformative. We find that all estimators can perform well under correct specification, all can be biased under misspecification, and misspecification is easiest to detect in the case of high-order moment estimators. We develop and demonstrate a minimum distance technique that extends the high-order moment estimators to be used on unbalanced panel data. Published by Oxford University Press 2011., Oxford University Press.

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Cite This Study

Erickson et al. (2011) studied this question.

synapsesocial.com/papers/6a2b4fe25a9d422e3d9dc891https://doi.org/10.1093/rfs/hhr120
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