Peelle’s Pertinent Puzzle (PPP) was described in 1987 in the context of estimating fundamental parameters that arise in nuclear interaction experiments. In PPP, generalized least squares (GLS) parameter estimates fell outside the range of the data, which has raised concerns that GLS is somehow flawed and has led to suggested alternatives to GLS estimators. However, there have been no correspond...

For a random e ects regression model with unbalanced panel data, we demonstrate that the Generalized Least Squares (GLS) estimator can be expressed as a (matrix) weighted average of estimators which utilize the within individual and the between individual variation in the data set. We thus generalize a relationship familiar for balanced panel data. Speci c attention must be given to the interce...

One challenge of analyzing samples of DNA sequences is to account for the nonnegligible polymorphisms produced by error when the sequencing error rate is high or the sample size is large. Specifically, those artificial sequence variations will bias the observed single nucleotide polymorphism (SNP) frequency spectrum, which in turn may further bias the estimators of the population mutation rate ...

Mortality rates are a widely used measure of hospital quality. A central problem with this measure is selection bias: simply put, severely ill patients may choose high quality hospitals. We control for severity of illness with an instrumental variables (IV) framework using geographic location data. We use IV to examine the quality of pneumonia care in Southern California from 1989 to 1994. We f...

In linear regression with heteroscedastic errors, the Generalized Least Squares (GLS) estimator is optimal, i.e., it is the Best Linear Unbiased Estimator (BLUE). The Ordinary Least Squares (OLS) estimator is suboptimal but still valid, i.e., unbiased and consistent. Halbert White, in his seminal paper (Econometrica, 1980) used the OLS residuals in order to obtain an estimate of the standard er...

Recently, path flow estimators (PFE) have been used for the estimation of origin–destination (O–D) matrices. This paper develops a formulation that incorporates a decoupled path flow estimator in a generalized least squares (GLS) framework. The approach seeks to solve a GLS problem that minimizes the sum of errors in traffic counts and O–D matrices based on an equilibrium assignment mapping der...

Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. These and previously proposed estimators of cointegrating vectors are used to study long-run U.S. ...

This paper studies the sensitivity of random effects estimators in the one-way error component regression model. Maddala and Mount (1973) give simulation evidence that in random effects models the properties of the feasible GLS estimator β̂ are not affected by the choice of the first-step estimator θ̄ used for the covariance matrix. Taylor (1980) gives a theoretical example of this effect. This p...

This paper introduces a new biased estimator, namely, almost unbiased Liu estimator (AULE) of β for the multiple linear regression model with heteroscedastics and/or correlated errors and suffers from the problem of multicollinearity. The properties of the proposed estimator is discussed and the performance over the generalized least squares (GLS) estimator, ordinary ridge regression (ORR) esti...

In this paper, I consider generalized least squares (GLS) estimation in fixed effects panel and multilevel models with autocorrelation. A complication which arises in implementing GLS estimation in these settings is that standard estimators of the covariance parameters necessary for obtaining the feasible GLS estimates will typically be inconsistent due to the inclusion of individual specific f...