New simulation results for the calibration and inverse median estimation problems

This paper gives the results of a new simulation study for the familiar calibration problem and the less familiar inverse median estimation problem. The latter arises when one wishes to estimate from a linear regression analysis the value of the independent variable corresponding to a specified value of the median of the dependent variable. For example, from the results of a regression analysis between stress and time to failure, one might wish to estimate the stress at which the median time to failure is 10,000 hours. In the study, the mean square error, Pitman closeness, and probability of overestimation are compared for both the calibration problem and the inverse median estimation problem for (1) the classical estimator, (2) the inverse estimator, and (3) a modified version of an estimator proposed by Naszodi (1978) for both a small sample and a moderately large sample situation.     

Should the individual voting records of central bankers be published?

We examine whether the publication of the individual voting records of central-bank council members is socially beneficial when the public is unsure about the efficiency of central bankers and central bankers are angling for re-appointment. We show that publication is initially harmful since it creates a conflict between socially desirable and individually optimal behavior for somewhat less efficient central bankers. However, after re-appointment, losses will be lower when voting records are published since the government can distinguish highly efficient from less efficient central bankers more easily and can make central bankers individually accountable. In our model, the negative effects of voting transparency dominate, and expected overall losses are always larger when voting records are published.     

Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments

A Bayesian approach for the many instruments problem in linear instrumental variable models is presented. The new approach has two components. First, a slice sampler is developed, which leverages a decomposition of the likelihood function that is a Bayesian analogue to two-stage least squares. The new sampler permits nonconjugate shrinkage priors to be implemented easily and efficiently. The new computational approach permits a Bayesian analysis of problems that were previously infeasible due to computational demands that scaled poorly in the number of regressors. Second, a new predictor-dependent shrinkage prior is developed specifically for the many instruments setting. The prior is constructed based on a factor model decomposition of the matrix of observed instruments, allowing many instruments to be incorporated into the analysis in a robust way. Features of the new method are illustrated via a simulation study and three empirical examples.     

Infants born full term and preterm increase the height of anti-gravity leg movements during a kick-activated mobile task using a scaffolded task environment

Prior research supports that infants born very preterm (PT), compared with full term (FT), have early differences in rate of learning and motor control that may hinder their ability to learn challenging motor tasks. Four-month-old infants born FT (n = 18) and PT (n = 18) participated in an infant kick-activated mobile task that was scaffolded to motivate progressively higher kicks. We found the FT group learned the association between their leg movements and mobile activation on the second day, but the PT group learned the association on the third day. Both groups of infants increased the height of their kicks on the day they learned the task, compared with their spontaneous kicking height. These findings suggest that infants born PT have the ability to learn challenging motor tasks, such as kicking high, when participating in a task environment that uses scaffolding.     

Firm-nonprofit collaboration: Explaining the rationale behind firms’ cross-sector partner choices

Cross-sector partnerships between firms and nonprofit organizations (NPOs) are emerging at an unprecedented rate to address sustainability issues. Extant research on cross-sector partnerships explains the firms' motivations to engage in partnerships but little is known about what drives the firms' partner choice. We build on firm-firm alliance research, differentiating between network-reinforcing and network-broadening strategies for finding partners. Based on a qualitative study with 33 experts from 17 frontrunner companies and 14 partnering NPOs, as well as secondary data, we assess the trade-offs between these two strategies and explore firms’ partner search and selection decisions. We advance cross-sector partnership research by proposing three drivers to explain why firms in our sample preferred to reinforce (rather than to broaden) their networks when forming additional cross-sector partnerships for sustainability. Moreover, we propose two conditions to explain why frontrunner firms often rely on opportunity-driven (rather than search-driven) partnership formation. These insights shed light on the type of formation strategy used in the context of cross-sector partnerships for sustainability.     

Optimal allocation of Monte Carlo simulations to multiple hypothesis tests

Statistics and Computing - Multiple hypothesis tests are often carried out in practice using p-value estimates obtained with bootstrap or permutation tests since the analytical p-values underlying...     

A Framework for Monte Carlo based Multiple Testing

We are concerned with a situation in which we would like to test multiple hypotheses with tests whose p‐values cannot be computed explicitly but can be approximated using Monte Carlo simulation. This scenario occurs widely in practice. We are interested in obtaining the same rejections and non‐rejections as the ones obtained if the p‐values for all hypotheses had been available. The present article introduces a framework for this scenario by providing a generic algorithm for a general multiple testing procedure. We establish conditions that guarantee that the rejections and non‐rejections obtained through Monte Carlo simulations are identical to the ones obtained with the p‐values. Our framework is applicable to a general class of step‐up and step‐down procedures, which includes many established multiple testing corrections such as the ones of Bonferroni, Holm, Sidak, Hochberg or Benjamini–Hochberg. Moreover, we show how to use our framework to improve algorithms available in the literature in such a way as to yield theoretical guarantees on their results. These modifications can easily be implemented in practice and lead to a particular way of reporting multiple testing results as three sets together with an error bound on their correctness, demonstrated exemplarily using a real biological dataset.     

An empirical study on the relationship between perceived employability and employee performance

Despite the growing attention to employability in the era of job insecurity, there have been a limited number of empirical studies regarding the positive effect of employees’ perceived employability (PE) on performance. The purpose of this study is to examine the relationship between PE and three domains of employee performance: in-role, adaptive, and extra-role. This study also tests the moderating effect of the perceived quality of employment on these relationships. To test our hypotheses, we surveyed 334 employees and their 37 immediate supervisors working for an organization in South Korea. We utilized official performance ratings and supervisors’ ratings to measure the three domains of performance. The results show that PE is positively related to in-role, adaptive, and extra-role performance. An interaction between PE and the perceived quality of employment on extra-role performance is also evident. Implications for research and human resource development practices will be provided.     

Jackknife and Analytical Bias Reduction for Nonlinear Panel Models

Fixed effects estimators of panel models can be severely biased because of the well‐known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed effects, as well as model parameters. We find large bias reductions from using these approaches in examples. We consider asymptotics where T grows with n, as an approximation to the properties of the estimators in econometric applications. We show that if T grows at the same rate as n, the fixed effects estimator is asymptotically biased, so that asymptotic confidence intervals are incorrect, but that they are correct for the panel jackknife. We show T growing faster than n^1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife.