Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

The Importance of Tax Adjustments When Evaluating Wage Expectations*

Using elicited expectations of future gross salaries, we evaluate characteristics causing German students to make larger or smaller estimation errors. While students seem to underestimate actual salaries by 18 percent, we show that these errors are highly attributable to misconceptions of the progressive income tax. Developing a suitable adjustment procedure, we correct students’ estimates and find that errors decline by 12 percentage points. Conducting regression analyses, we reveal strong connections with students’ age, gender, work experience, secondary school track, and knowledge about student loans. These results change notably if not controlling for students’ misconceptions of the tax system.     

The effect of entrepreneurship education in schools on entrepreneurial outcomes: a systematic review

Management Review Quarterly - This study systematically reviewed 21 quantitative and qualitative articles on the effect of entrepreneurship education in schools. We reviewed short-term effects,...     

Libra — A Differentiated View on Facebook’s Virtual Currency Project

Intereconomics - Libra — a global virtual currency project initiated by Facebook — has been the subject of many controversial discussions since its announcement in June 2019. This paper...     

Antecedents to Decision‐Making Quality and Agility in Innovation Portfolio Management

Innovation portfolio management (IPM) is a dynamic decision‐making process, in which projects are evaluated and selected, and resources are allocated. Previous research has developed an understanding of IPM success and its influencing factors. However, little research investigated the quality of the decision‐making process and the ability to quickly adapt the portfolio. This study focuses on the antecedents of decision‐making quality and agility (i.e., responsiveness to changes in the environment). Based on a decision‐making framework, five structural and cultural IPM components are derived as important antecedents of decision‐making quality, which in turn influences agility. The structural components (1) clarity of strategic goals, (2) formality of the IPM processes, and (3) controlling intensity serve a coordinating function. The cultural components (4) innovation climate and (5) risk climate serve a motivating function in IPM. An analysis of a sample of 179 firms and their innovation portfolios through structural equation modeling using a double‐informant design documents that these five components all positively influence portfolio decision‐making quality, which in turn positively influences agility. Results further show that environmental turbulence moderates some of these relationships. While the positive effect of process formality is weakened under increasing turbulence, the effects of controlling intensity and climate for innovation are strengthened by environmental turbulence. The findings have theoretical implications for the understanding of IPM as a dynamic capability and practical implications for the management of portfolios in turbulent environments.