Differentiating transformational and non-transformational leaders on the basis of neurological imaging

The purpose of this study was to evaluate the viability of using neurological imaging to classify transformational leaders, versus non-transformational leaders, as identified through existing psychometric methods. Specifically, power spectral analysis measures based on electroencephalograms (EEG) were used to develop and validate a discriminant function that can classify individuals according to their transformational leadership behavior. Resting, eyes closed EEG was recorded from 19 scalp locations for 200 civilian and military leaders. We also assessed follower or peer perceptions of transformational leadership through the use of the Multifactor Leadership Questionnaire (MLQ). Our discriminant analysis, which involved a two-step, neural variable reduction and selection process, was 92.5% accurate in its classification of leaders. Patterns in the spectral measures of the brain of leaders, including activity and network dynamic metrics, are discussed as potential correlates of transformational leadership behavior. The current work provides a better understanding of the latent and dynamic neurological mechanisms that may underpin the transformational leadership qualities of individuals.     

A generalized linear model approach to seasonal aspects of wind speed modeling

The aim of the article is to identify the intraday seasonality in a wind speed time series. Following the traditional approach, the marginal probability law is Weibull and, consequently, we consider seasonal Weibull law. A new estimation and decision procedure to estimate the seasonal Weibull law intraday scale parameter is presented. We will also give statistical decision-making tools to discard or not the trend parameter and to validate the seasonal model.     

Asymptotic Normality of Extreme Quantile Estimators Based on the Peaks-Over-Threshold Approach

The POT (Peaks-Over-Threshold) approach consists of using the generalized Pareto distribution (GPD) to approximate the distribution of excesses over thresholds. In this article, we establish the asymptotic normality of the well-known extreme quantile estimators based on this POT method, under very general assumptions. As an illustration, from this result, we deduce the asymptotic normality of the POT extreme quantile estimators in the case where the maximum likelihood (ML) or the generalized probability-weighted moments (GPWM) methods are used. Simulations are provided in order to compare the efficiency of these estimators based on ML or GPWM methods with classical ones proposed in the literature.     

A Note on Sufficient Conditions for Valid Unmodified t Testing in Correlation Analysis with Autocorrelated and Heteroscedastic Sample Data

Traditionally, sphericity (i.e., independence and homoscedasticity for raw data) is put forward as the condition to be satisfied by the variance–covariance matrix of at least one of the two observation vectors analyzed for correlation, for the unmodified t test of significance to be valid under the Gaussian and constant population mean assumptions. In this article, the author proves that the sphericity condition is too strong and a weaker (i.e., more general) sufficient condition for valid unmodified t testing in correlation analysis is circularity (i.e., independence and homoscedasticity after linear transformation by orthonormal contrasts), to be satisfied by the variance–covariance matrix of one of the two observation vectors. Two other conditions (i.e., compound symmetry for one of the two observation vectors; absence of correlation between the components of one observation vector, combined with a particular pattern of joint heteroscedasticity in the two observation vectors) are also considered and discussed. When both observation vectors possess the same variance–covariance matrix up to a positive multiplicative constant, the circularity condition is shown to be necessary and sufficient. “Observation vectors” may designate partial realizations of temporal or spatial stochastic processes as well as profile vectors of repeated measures. From the proof, it follows that an effective sample size appropriately defined can measure the discrepancy from the more general sufficient condition for valid unmodified t testing in correlation analysis with autocorrelated and heteroscedastic sample data. The proof is complemented by a simulation study. Finally, the differences between the role of the circularity condition in the correlation analysis and its role in the repeated measures ANOVA (i.e., where it was first introduced) are scrutinized, and the link between the circular variance–covariance structure and the centering of observations with respect to the sample mean is emphasized.     

Too Big to Learn: The Effects of Major Acquisition Failures on Subsequent Acquisition Divestment

We examine whether firms learn from their major acquisition failures. Drawing from a threat‐rigidity theoretical framework, we suggest that firms do not learn from their major acquisition failures. Furthermore, we hypothesize that host‐country experience reinforces the negative effects of major acquisition failures. Our research hypotheses are tested using an event history analysis of 741 acquisitions undertaken by French listed and non‐listed firms in the USA between January 1988 and December 2008. We use failure divestment (divestment resulting from acquisition failure) as a proxy for acquisition performance. Consistent with our theoretical framework, we find that major acquisition failures have a negative impact on future acquisition performance. Furthermore, we find that such negative effects are reinforced by firms’ host‐country experience.     

SOME QUALMS ABOUT THE TEST OF THE INSTITUTIONALIST HYPOTHESIS OF THE LONG-RUN BEHAVIOR OF VELOCITY

This paper expresses econometric qualms about Bordo and Jonung's [1981] analysis of long-run velocity. They did not recognize that, for U.S. and Canadian data, the log of velocity has a unit root. Hence, estimation of a log level regression may produce spurious regressions.
When Bordo and Jonung's velocity equation is reestimated in rate of change form, permanent income is significant, contrary to their earlier conclusion. Moreover, using this approach gives a stronger result for one of the institutional variables in the velocity function, in the sense that the remaining variables become more significant.