52 Pages Posted: 23 Mar 2010 Last revised: 6 Apr 2010
Date Written: March 16, 2010
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 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.
Keywords: Investment equations, measurement error, Monte Carlo simulations, instrumental variables, GMM
JEL Classification: G31, C23
Suggested Citation: Suggested Citation
Almeida, Heitor and Campello, Murillo and Galvao, Antonio F., Measurement Errors in Investment Equations (March 16, 2010). Available at SSRN: https://ssrn.com/abstract=1572804 or http://dx.doi.org/10.2139/ssrn.1572804