Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size. For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step algorithms involving least squares fitting and hard thresholding. The algorithms are methodological...

Generalized least-squares (GLS ) regression extends ordinary least-squares (OLS) estimation of the normal linear model by providing for possibly unequal error variances and for correlations between different errors. A common application of GLS estimation is to time-series regression, in which it is generally implausible to assume that errors are independent. This appendix to Fox and Weisberg (2...

Instrumental Variables (IV) estimates are frequently used to correct Ordinary Least Squares (OLS) estimates for simultaneity bias. This paper argues that in many instances, in particular when the endogenous variable is a dummy variable, the measure derived overestimates the effect. It presents a simple method for obtaining a correct estimate and applies this method to data of voluntary health i...

We investigate estimation and inference in difference in difference econometric models used in the analysis of treatment effects. When the innovations in such models display serial correlation, commonly used ordinary least squares (OLS) procedures are inefficient and may lead to tests with incorrect size. Implementation of feasible generalized least squares (FGLS) procedures is often hindered b...

This paper presents a hyperbolic trigonometric (HT) transformation procedure for empirically estimating a cumulative probability distribution function (cdf), from which the probability density function (pdf) can be obtained by differentiation. Maximum likelihood (ML) is the appropriate estimation technique, but a particularly appealing feature of the HT transformation as opposed to other zero-o...

Volterra lters have been applied to many nonlinear system identiication problems. However, obtaining good lter estimates from short and/or noisy data records is a diicult task. We propose a penalized least squares estimation algorithm and derive appropriate penalizing functionals for Volterra lters. An example demonstrates that penalized least squares estimation can provide much more accurate l...

This paper considers parametric inference in a wide range of structural econometric models. It illustrates how the indirect inference principle can be used in the inference of these models. Specifically, we show that an ordinary least squares (OLS) estimation can be used as an auxiliarymodel, which leads to amethod that is similar in spirit to a two-stage least squares (2SLS) estimator. Monte C...

The poor academic performance of students of Nigerian tertiary institution has been a major concern. This paper, however, considers the mode of selection for admission via the University Matriculation Examination (by JAMB), the Post University Matriculation Examination, as well as academic performance after first year in the University. Using the Ordinary Least Square (OLS) method of estimating...