MeasurementError - If both (1) and (3) are true, the...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
Sarah Hamersma 3/7/05 Intro to Measurement Error Issues (based on Hausman (JEP 2001) and textbook info) The Point: Sometimes the data we have do not do a perfect job of measuring the variable of interest, due to things like people’s survey responses being imprecise. This may be true about variables on the LHS or RHS in the regression, but the implications of each are different. A number of ways have been proposed to deal with the problems created by measurement error. Main issues: (see additional pages for the derivations we did in class) 1) Classical measurement error (in X) -- OLS will underestimate the true effect - note that the coefficients on the other variables will be biased in unknown direction - note that if more than one variable has measurement error, we have a mess 2) Getting an upper bound on correct coefficient in cases of classical measurement error 3) Measurement error in Y – results in unbiased coefficient, but loss of efficiency (big SEs)
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: If both (1) and (3) are true, the results are as expected (underestimated coeffs with large SEs). IV as a Solution to Mismeasurement: Caution The weak instruments problem can become ugly here, just as when IV is used for other sorts of endogeneity problems. Hahn and Hausman present a spec test – after estimating the model with 2SLS, do a 2SLS version of the reverse regression. Results should not differ much (otherwise the problem hasn’t been solved). Measurement Error in the LHS Variables: Probit and Logit If we have a binary dependent variable w/ measurement error (i.e. some people are misclassified), the results will be biased and inconsistent. Hausman and others provide some suggestions. Note: for the case where the LHS is mismeasured but continuous, we will have problems with quantile regression while OLS will remain unbiased....
View Full Document

This note was uploaded on 01/10/2011 for the course ECON 7427 taught by Professor Hamersma during the Spring '06 term at University of Florida.

Ask a homework question - tutors are online