Wynne W. Chin • Barbara L. Marcolin • Peter R. Newsted C. T. Bauer College of Business, University of Houston, Houston, Texas 77204 Haskayne School of Business, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4 Centre for Innovative Management, Athabasca University, 22 Sir Winston Churchill Avenue, St. Alberta, Alberta, Canada, T8N 1B4 [email protected] • marcolin@uc...

The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a large percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory...

Nonlinear Regime Switching models are becoming increasingly popular in recent applied literature, as they allow capturing state-dependent behaviors which would be otherwise impossible to model. However, despite their popularity, the specification and estimation of these type of models is computationally complex and it is far from being a univocally solved issue. This paper aims at contributing ...

This paper presents a Monte Carlo comparison of several versions of heteroscedasticity robust standard errors (HRSEs) to a nonparametric feasible generalized least squares procedure (NPGLS). Results suggest that the NPGLS procedure provides an improvement in efficiency ranging from 3% to 12% or more in reasonable sample sizes using simple functional forms for heteroscedasticity. This results in...

The paper develops a new estimation of non-parametric regression functions for clustered or longitudinal data. We propose to use Cholesky decomposition and profile least squares techniques to estimate the correlation structure and regression function simultaneously. We further prove that the estimator proposed is as asymptotically efficient as if the covariance matrix were known. A Monte Carlo ...

It is well known that the ordinary least-squares estimates (OLSE) of autoregressive models are biased in small sample. In this paper, an attempt is made to obtain the unbiased estimates in the sense of median or mean. Using Monte Carlo simulation techniques, we extend the median-unbiased estimator proposed by Andrews (1993, Econometrica 61 (1), 139–165) to the higher-order autoregressive proces...

This short paper briefly describes the implementation of the least squares Monte Carlo method in the rlsm package. This package provides users with an easy manner to experiment with the large amount of R regression tools on any regression basis and reward functions. This package also computes lower and upper bounds for the true value function via duality methods.

This paper investigates howstandard residual based tests for cointegration— under structural change in the long run relationship—canbemodified in order to reduce size distortions and improve power, by following the same ideas used in the unit root context. This is a natural strategy given that these tests are unit root statistics applied to estimated residuals from a cointegrating regression. I...

In this paper we propose a whole class of estimators (clockwise repeated median estimators or CRM) for the simple regression model that are immune to manipulation by the agents generating the data. Although strategic considerations affecting the stability of the estimated parameters in regression models have already been studied (the Lucas critique), few efforts have been made to design estimat...