When a company goes public, it is standard practice that the underwriting syndicate allocates more shares than are issued. The underwriter thus holds a short position that it commonly fills by aftermarket trading when market prices fall or, when prices rise, by executing the so-called overallotment option. This option is a standard feature of initial public offering (IPO) arrangements that allows the underwriter to purchase more shares from the issuer at the original offer price. We propose a theoretical model to study the implications of this combination of short position and overallotment option on the pricing of the IPO. Maximizing the sum of both the profits from their share of the offer revenue and the potential profits from aftermarket trading, we show that underwriters strategically distort the offer price. This results either in exacerbated underpricing when favorably informed underwriters lower prices to secure a signaling benefit, or in informationally inefficient offer prices when underwriters pool in offer prices irrespective of their information.
Partnerships are the prevalent organizational form in many industries. Profits are most frequently shared equally among the partners. The purpose of our paper is to provide a rationale for equal sharing rules. We show that with inequity averse partners the equal sharing rule is the unique sharing rule that maximizes the partners' incentives to exert effort. We further show that inequity aversion can enhance efficiency in partnerships of given size, but that it can also cause partnerships to be inefficiently small.
Studies on the effect of ageing on health care expenditure (HCE) have revealed the importance of controlling for time-to-death (TTD). These studies, however, are subject to possible endogeneity if HCE influences the remaining life expectancy. This paper introduces a 10-year observation period on monthly HCE, socioeconomic characteristics and survivor status to first predict TTD and then use the predicted values as an instrument in the regression for HCE. While exogeneity of TTD has to be rejected, core results concerning the role of TTD rather than age as a determinant of HCE (the ‘red herring’ hypothesis) are confirmed.
Die Wirtschaft am Standort Schweiz steht in einem intensiven
internationalen Innovationswettbewerb. Eine gute Zusammenarbeit zwischen Hochschulen und Unternehmen im Bereich Forschung und Entwicklung (F&E)ist dabei besonders wichtig, damit die sich bietenden Chancen genutzt
werden können. Allerdings sind damit auch Probleme verbunden
– insbesondere im Umgang mit dem geistigen Eigentum, das
aus einer solchen Kooperation entsteht. Im Mai 2009 hat das
Bundesamt für Berufsbildung und Technologie (BBT) in Zusammenarbeit mit dem Staatssekretariat für Bildung und Forschung (SBF)und dem Forschungs- und Beratungsunternehmen
Infras eine Befragung zu diesem Thema durchgeführt.
We discuss a class of copula-based ordered probit models with endogenous switching. Such models can be useful for the analysis of self-selection in subjective well-being equations in general, and job satisfaction in particular, where assignment of regressors may be endogenous rather than random, resulting from individual maximization of well-being. In an application to public and private sector job satisfaction, and using data on male workers from the German Socio-Economic Panel for 2004, and using two alternative copula functions for dependence, we find consistent evidence for endogenous sector selection.
The paper provides estimates of the effect of economic inequality on middle class well being in Switzerland. Economic well being is proxied by a person's satisfaction with his/her income. Two inequality indicators are used, one standard (the Gini coefficient of the pre-tax income distribution) and one novel (the number of luxury car registrations per 1000 population). Identification is through cross-sectional variation of these indicators at various levels of spatial aggregation. Results using data from the Swiss Household Panel confirm the existence of a robust inverse relationship between inequality and satisfaction among the middle class.
Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context-dependent changes in connection strengths.
Conventional decoding methods in neuroscience aim to predict discrete brain states from multivariate correlates of neural activity. This approach faces two important challenges. First, a small number of examples are typically represented by a much larger number of features, making it hard to select the few informative features that allow for accurate predictions. Second, accuracy estimates and information maps often remain descriptive and can be hard to interpret. In this paper, we propose a model-based decoding approach that addresses both challenges from a new angle. Our method involves (i) inverting a dynamic causal model of neurophysiological data in a trial-by-trial fashion; (ii) training and testing a discriminative classifier on a strongly reduced feature space derived from trial-wise estimates of the model parameters; and (iii) reconstructing the separating hyperplane. Since the approach is model-based, it provides a principled dimensionality reduction of the feature space; in addition, if the model is neurobiologically plausible, decoding results may offer a mechanistically meaningful interpretation. The proposed method can be used in conjunction with a variety of modelling approaches and brain data, and supports decoding of either trial or subject labels. Moreover, it can supplement evidence-based approaches for model-based decoding and enable structural model selection in cases where Bayesian model selection cannot be applied. Here, we illustrate its application using dynamic causal modelling (DCM) of electrophysiological recordings in rodents. We demonstrate that the approach achieves significant above-chance performance and, at the same time, allows for a neurobiological interpretation of the results.
Treatment analyses based on average outcomes do not immediately generalize to the case of ordered responses because the expectation of an ordinally measured variable does not exist. The proposed remedy in this paper is a shift in focus to distributional effects. Assuming a threshold crossing model on both the ordered potential outcomes and the binary treatment variable, and leaving the distribution of error terms and functional forms unspecified, the paper discusses how the treatment effects can be bounded. The construction of bounds is illustrated in a simulated data example.
"Consider the problem of testing s hypotheses simultaneously. In this paper, we derive methods which control the generalized familywise error rate given by the probability of k or more false rejections, abbreviated k-FWER. We derive both single-step and stepdown procedures that control the k-FWER in finite samples or asymptotically, depending on the situation. Moreover, the procedures are asymptotically balanced in an appropriate sense. We briefly consider control of the average number of false rejections. Additionally, we considernthe false discovery proportion (FDP), defined as the number of false rejections divided by the total number of rejections (and defined to be 0 if there are no rejections). Here, the goal is to construct methods which satisfy, for given γ and α, P{FDP > γ} ≤ α, at least asymptotically. Special attention attention is paid to the construction of methods which implicitly take into account the dependence structure of the individual test statistics in ordernto further increase the ability to detect false null hypotheses. A general resampling and subsampling approach is presented which achieves these objectives, at least asymptotically."
