The effects of measurement error on two-stage, least-squares estimates

Abstract

Two-stage least squares (2SLS) is a statistical procedure that is used to correct for simultaneity bias and errors in variables. When applied to certain kinds of models, however, 2SLS is itself susceptible to bias as a result of random and nonrandom measurement error in the data. Using data from the 1980 Center for Political Studies panel, I show how different assumptions about measurement error produce radically different impressions about the reciprocal relationship between party identification and presidential performance evaluations. © 1991 by the University of Michigan.