Peer Groups and Bias Detection in Least Squares Regression

A correlation between regressors and disturbances presents challenging problems in linear regression. In the context of spatial econometrics LeSage and Pace (2009) show that an autoregressive model estimated by maximum likelihood may be able to detect least squares bias. I suggest that spatial neighbors can be replaced by “peer groups” as in Blankmeyer et al. (2011), thereby extending considerably the range of contexts where the autoregressive model can be utilized. The procedure is applied to two data sets and in a simulatio

Explorations in NISE Estimation

. Ordinary least squares, two-stage least squares and the NISE estimator are applied to three data sets involving equations from microeconomics and macroeconomics. The focus is on simultaneity bias in linear least squares and on the ability of the other estimators to mitigate the bias

How robust is linear regression with dummy variables? Working Paper

Abstract Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations. Such groups may easily include enough outliers to break down the parameter estimates. Models with many fixed or random effects appear to be especially vulnerable to outlying data. This paper discusses the problem at an intuitive level and cites sources for the key theorems establishing bounds on the breakdown point in models with dummy variables

A Heisenberg Bound for Stationary Time Series

Heisenberg's principle of indeterminacy is applied to stationary time series models. The position and velocity of a forecast are defined and are shown to be imperfectly correlated. Then a first-order autoregression is used to illustrate the trade-off between precision of position and precision of velocity. A counterpart of Planck's constant is identified, and the Heisenberg bound is derived for several autoregressive moving- average models. The time-energy version of the Heisenberg principle is discussed in the context of a stationary model in continuous time.Stationary time series Heisenberg uncertainty principle