Exogeneity testing is studied in the presence of outliers in response variables. Robust tests based on least absolute deviations (LAD) and M estimators are proposed and illustrated with an application to Mroz (1987) data. Our simulation results show that the proposed robust tests outperform the traditional Hausman test for exogeneity in terms of empirical power in the presence of outliers in re...

ÐThe paper describes a new, logic-based methodology for analyzing observations. The key features of the Logical Analysis of Data (LAD) are the discovery of minimal sets of features necessary for explaining all observations and the detection of hidden patterns in the data capable of distinguishing observations describing apositiveo outcome events from anegativeo outcome events. Combinations of s...

A number of previous studies have shown that a combination of forecasts typically outperforms any component forecast. Service managers may wish to use forecast combination to improve forecast accuracy in predicting retail sales. In this study, revenue data from an actual service company is used to generate and test a least absolute value (LAV) regression model for forecast combination. The LAV ...

This paper discusses some asymptotic uniform linearity results of randomly weighted empirical processes based on long range dependent random variables+ These results are subsequently used to linearize nonlinear regression quantiles in a nonlinear regression model with long range dependent errors, where the design variables can be either random or nonrandom+ These, in turn, yield the limiting be...

Ordinary Least Squares (OLS) is omnipresent in regression modeling. Occasionally Least Absolute Deviations (LAD) or other methods are used as an alternative when there are outliers. Although some data adaptive estimators have been proposed they are typically difficult to implement. In this note, we propose an easy to compute adaptive estimator which is simply a linear combination of OLS and LAD...

The Lasso is an attractive approach to variable selection in sparse, highdimensional regression models. Much work has been done to study the selection and estimation properties of the Lasso in the context of least squares regression. However, the least squares based method is sensitive to outliers. An alternative to the least squares method is the least absolute deviations (LAD) method which is...

We propose a general estimation principle based on the assumption that instrumental variables (IV) do not explain the error term in a structural equation. The estimators based on this principle is independent of the normalization constraint, unlike the standard IV estimators such as the two-stage least squares estimator. Using the new principle, we propose the L1 IV estimator, which is an IV es...