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 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...     

Unconscious Processes of Organizing: Intergroup Conflict in Mental Health Care

A critical but overlooked issue in Weick’s seminal work, The Social Psychology of Organizing (1969/1979), concerns ‘the heat’ of organizing processes, namely, the underground emotional processes underpinning the organizing of conflictual work relationships. We present a qualitative case study of psychiatric agencies mandated by public policy to collaborate but instead engaged in persistent conflict despite its deleterious effects on their working relationship and on the wellbeing of the clients they intended to serve. To explain these conflictual features of organizing, we integrate Weick’s organizing theory with systems psychodynamics to deepen the understanding of emotions in organizing, specifically the motivational forces underpinning sensemaking and actions between interacting psychiatric agencies. This integration of theories reveals a critical feature of the relationship between the conscious and unconscious organizing processes: When a threat is involved, sensemaking and action are overtaken by social defences, resulting in dysfunctional organizing of the primary task. Drawing on these findings, we enrich Weick’s seminal work by developing a model that portrays organizing as the ritualized interaction of emotions, sensemaking and behavioural responses.     

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.     

Do casinos pay their customers to become risk-averse? Revising the house money effect in a field experiment

The house money effect predicts that individuals show increased risk-seeking behavior in the presence of prior windfall gains. Although the effect’s existence is widely accepted, experimental studies that compare individuals’ risk-taking behavior using house money to individuals’ risk-taking behavior using their own money produce contradictory results. This experimental field study analyzes the gambling behavior of 917 casino customers who face real losses. We find that customers who received free play at the entrance showed not higher but significantly lower levels of risk-taking behavior during their casino visit, expressed through lower average wagers. This study thus provides field evidence against the house money effect. Moreover, as a result of lower levels of risk seeking, endowed customers yield better economic results in the form of smaller own-money losses when leaving the casino.     

Marginalized Predictive Likelihood Comparisons of Linear Gaussian State‐Space Models with Applications to DSGE,DSGE‐VAR,and VAR Models

The predictive likelihood is useful for ranking models in forecast comparison exercises using Bayesian inference. We discuss how it can be estimated, by means of marzginalization, for any subset of the observables in linear Gaussian state‐space models. We compare macroeconomic density forecasts for the euro area of a DSGE model to those of a DSGE‐VAR, a BVAR and a multivariate random walk over 1999:Q1–2011:Q4. While the BVAR generally provides superior forecasts, its performance deteriorates substantially with the onset of the Great Recession. This is particularly notable for longer‐horizon real GDP forecasts, where the DSGE and DSGE‐VAR models perform better. Copyright © 2016 John Wiley & Sons, Ltd.