arXiv subjects
Publications and source records attributed to Eric Blankmeyer.
Linear least squares regression is subject to bias due to an omitted variable, a mismeasured regressor, or simultaneity. A simple test to detect the bias is proposed and explored in simulation and in real data sets.
An economic model of crime is used to explore the consistent estimation of a simultaneous linear equation without recourse to instrumental variables. A maximum-likelihood procedure (NISE) is introduced, and its results are compared to ordinary least squares and two-stage least squares. The paper is motivated by previous research on the crime model and by the well-known practical problem that valid instruments are frequently unavailable.
Errors-in-variables is a long-standing, difficult issue in linear regression; and progress depends in part on new identifying assumptions. I characterize measurement error as bad-leverage points and assume that fewer than half the sample observations are heavily contaminated, in which case a high-breakdown robust estimator may be able to isolate and down weight or discard the problematic data. In simulations of simple and multiple regression where eiv affects 25% of the data and R-squared is mediocre, certain high-breakdown estimators have small bias and reliable confidence intervals.
For a single equation in a system of linear equations, estimation by instrumental variables is the standard approach. In practice, however, it is often difficult to find valid instruments. This paper proposes a maximum likelihood method that does not require instrumental variables; it is illustrated by simulation and with a real data set.