Penalized likelihood smoothing in robust state space models

In likelihood-based approaches to robustify state space models, Gaussian error distributions are replaced by non-normal alternatives with heavier tails. Robustified observation models are appropriate for time series with additive outliers, while state or transition equations with heavy-tailed error distributions lead to filters and smoothers that can cope with structural changes in trend or slope caused by innovations outliers. As a consequence, however, conditional filtering and smoothing densities become analytically intractable. Various attempts have been made to deal with this problem, reaching from approximate conditional mean type estimation to fully Bayesian analysis using MCMC simulation. In this article we consider penalized likelihood smoothers, this means estimators which maximize penalized likelihoods or, equivalently, posterior densities. Filtering and smoothing for additive and innovations outlier models can be carried out by computationally efficient Fisher scoring steps or iterative Kalman-type filters. Special emphasis is on the Student family, for which EM-type algorithms to estimate unknown hyperparameters are developed. Operational behaviour is illustrated by simulation experiments and by real data applications. Received: March 1998     

Letting Go to Grow—Empirical Findings on a Hearsay

Research to date has largely neglected the role of management style in new entrepreneurial ventures. We address this gap by investigating the impact of management style on company performance and how the impact is different contingent on the life cycle stage of new entrepreneurial ventures. By employing the concept of microinvolvement, our study demonstrates that management style significantly impacts company performance and that the merits of a highly involved management style are less in later‐stage new entrepreneurial ventures. This finding provides support for the saying “letting go to grow” and raises important implications for management.     

Farm-based modelling of the EU sugar reform: impact on Belgian sugar beet suppliers

A mathematical programming model, calibrated on individual farmdata, is used to analyse the reform of the common market organisation(CMO) in the sugar sector of the European Union. The model includesa precautionary farm supply function for out-of-quota sugarbeet that is estimated as part of a simultaneous system of first-orderconditions. Simulation results from a sample of Belgian sugarbeet farms show that the sugar CMO reform induces differentsupply and income effects across farms depending on their shareof out-of-quota sugar beet relative to their total beet supplyand their quota rent. A further cut in the minimum price ofsugar beet initiates structural change in the farm sector.     

The role of business schools in managing the incongruence between doing what is right and doing what it takes to get ahead

This paper accepts as given that business students want to get ahead. It criticizes business schools for their failure to reduce the incongruence between doing what is right and doing what it takes to get ahead. Because of this failure business school graduates carry negative ideas, attitudes and behaviors vis-à-vis social responsibility from business schools into the business world. Recommendations are made for increasing the social responsibility of business schools. Robert H. Schwartz is an Associate Professor of Management and Coordinator of the Health Care Administration Program at the University of Toledo. He is the author of Coping With Unbalanced Information About Decision Making Influence For Nurses, Hospital and Health Services Administration (Winter, 1990), and of other papers pertaining to health administration and career choice.Sami Kassem is Professor of Management at the University of Toledo.Dean Ludwig is Assistant Professor of Management at the University of Toledo.