Measurement Errors in Investment Equations
We use Monte Carlo simulations and real data to assess the performance of alternative methods that deal with measurement error in investment equations. Our experiments show that individual-fixed effects, error heteroscedasticity, and data skewness severely affect the performance and reliability of methods found in the literature. In particular, estimators that use higher-order moments are shown to return biased coefficients for (both) mismeasured and perfectly-measured regressors. These estimators are also very inefficient. Instrumental variables-type estimators are more robust and efficient, although they require fairly 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 across different specifications and not economically meaningful. Instrumental variables methods yield estimates that are robust and seem to conform to theoretical priors. Our analysis provides guidance for dealing with the problem of measurement error under circumstances empirical researchers are likely to find in practice.
|Date of creation:||Apr 2010|
|Date of revision:|
|Publication status:||published as Measurement Errors in Investment Equations (with H. Almeida and A. Galvao), Review of Financial Studies, 2010 (23), 3279-3328. [Lead article.]|
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"Instrumental variables and GMM: Estimation and testing,"
Boston College Working Papers in Economics
545, Boston College Department of Economics, revised 14 Feb 2003.
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