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In order to gain a deeper understanding of how emotional dynamics play out in organizations, a better understanding of the underlying structure of emotions in the workplace is needed. This study set out to investigate the emotional reality of work teams that are confronted with organizational change and to create a feeling scale that can be used to analyze and evaluate the emotional experience of employees involved in and affected by the change. This chapter outlines the results of an iterative statistical analysis to determine the underlying structure of emotions and basic dimensions on which emotions can be categorized. Feeling scales ranging in length from 22 to 42 feeling items were answered by up to 26,900 respondents as part of employee surveys that were used to investigate the subjective perception of organizational change. Factor analysis and self-organizing maps (SOMs) analysis were used in order to cluster and differentiate the underlying basic categories of emotions. The results show that feelings are mainly differentiated as either positive or negative and that those two main factors consist of seven underlying categories, which are summarized as the emotion scales: “Passion,” “Drive,” “Curiosity,” “Defiance,” “Anger,” “Fear and Distress,” and “Damage.” The basic dimensions of the emotions were “hedonic tone” and “affective focus.”

In this paper, we follow the same logic as in Hausman (1978) to create a testing procedure that checks for the presence of outliers by comparing a regression estimator that is robust to outliers (S-estimator), with another that is more efficient but affected by them. Some simulations are presented to illustrate the good behavior of the test for both its size and its power.

The authors provide new evidence in favor of the expectation hypothesis (EH) as a long-run theory of the term structure of interest rates. Using nonparametric techniques first, the authors show that the results of conventional tests that reject EH are strongly affected by the presence of extreme observations – only a handful in the case of longer maturities. The authors then provide a new general methodology that determines the number of outliers causing any theory to fail, and their approach quantifies the extent of this failure.

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogs otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.

This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

We propose a novel dynamic factor model to characterise comovements between returns on securities from different asset classes from different countries. We apply a global-class-country latent factor model and allow time-varying loadings. We are able to separate contagion (asset exposure driven) and excess interdependence (factor volatility driven). Using data from 1999 to 2012, we find evidence of contagion from the US stock market during the 2007–2009 financial crisis, and of excess interdependence during the European debt crisis from May 2010 onwards. Neither contagion nor excess interdependence is found when the average measure of model implied comovements is used.

As we saw in Chapter 3, there are two aspects of individualism and “personhood.” In the first instance, “personhood” gives individuals more options in negotiating their identities in society. But “individualism” also means that persons are also held individually liable for the achievement of societal goals. Displaying civic responsibility is after all part of the modern definition of proper citizens.