Disentangling Morphology, Star Formation, Stellar Mass, and Environment in Galaxy Evolution

We present a study of the spectroscopic and photometric properties of galaxies in six nearby clusters. We perform a partial correlation analysis on our dataset to investigate whether the correlation between star formation rates in galaxies and their environment is merely another aspect of correlations of morphology, stellar mass, or mean stellar age with environment, or whether star formation rates vary independently of these other correlations. We find a residual correlation of ongoing star formation with environment, indicating that even galaxies with similar morphologies, stellar masses, and mean stellar ages have lower star formation rates in denser environments. Thus, the current star formation gradient in clusters is not just another aspect of the morphology-density, stellar mass-density, or mean stellar age-density relations. Furthermore, the star formation gradient cannot be solely the result of initial conditions, but must partly be due to subsequent evolution through a mechanism (or mechanisms) sensitive to environment. Our results constitute a true ``smoking gun'' pointing to the effect of environment on the later evolution of galaxies.Comment: 31 pages, including 5 figures; accepted for publication in Ap

American trade policy towards Sub Saharan Africa –- a meta analysis of AGOA

Twelve econometric studies investigating the impact of agoa presented in this paper have reported 174 different estimates. In testing for publication bias and whether there is a genuine empirical impact of agoa we resort to a meta-analysis. The meta-analysis provides us with a formal means of testing for publication bias and an empirical effect. The result shows significant publication bias in the selected studies. However, in a few cases the test for a genuine effect is passed successfully. The results of the meta-analysis indicates that agoa increased the trade of beneficiaries by 13.2%

Empirical Capital Structure Research: New Ideas, Recent Evidence, and Methodological Issues

Even 50 years after Modigliani/Miller’s irrelevance theorem, the basic question of how firms choose their capital structure remains unclear. This survey paper aims at summarizing and discussing corresponding recent developments in empirical capital structure research, which, in our view, are promising for future research. We first present some “stylized facts” on capital structure issues. The focus of the discussion is set on studies taking on the key idea to differentiate between competing theories by testing for firm adjustment behavior following shocks to their capital structure. In addition, we discuss empirical studies examining additional factors that may influence capital structure decisions, but have gained only recently attention in the literature (like corporate ratings or irrational managers). Since some of the available contradictory evidence on capital structure issues might be explained by econometric challenges due to the typical data structure, we also discuss methodological issues like panel data, endogeneity, and partial adjustment models in the capital structure context. Finally, we illustrate the methodological and empirical aspects discussed in this survey by providing corresponding evidence for exchange-listed German companies in the period 1987-2006

Direct Demand Models of Air Travel: A Novel Approach to the Analysis of Stated Preference Data

This paper uses what has been termed the direct demand approach to obtain elasticity estimates from discrete choice Stated Preference data. The Stated Preference data relates to business travellers' choices between air and rail. The direct demand methodology is outlined and some potential advantages over the conventional disaggregate logit model are discussed. However, further research regarding the relative merits of the two approaches is recommended. The direct demand model is developed to explain variations in the demand for air travel as a function of variations in air headway and cost and in train journey time, frequency, interchange and cost. Relatively little has previously been published about the interaction between rail and air and the elasticities and variation in them which have been estimated are generally plausible. In particular, the results show that large improvements in rail journey times can have a very substantial impact on the demand for air travel and that the rail journey time cross-elasticity depends on satisfying a three hour journey time threshold

Automated financial multi-path GETS modelling

General-to-Specific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multi-path GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multi-path GETS modelling in finance. We provide a result with associated methods that overcome many of the problems, and develop a simple but general and flexible algorithm that automates financial multi-path GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential variance specification can include log-ARCH terms, log-GARCH terms, asymmetry terms, Bernoulli jumps and other explanatory variables, the algorithm we propose returns parsimonious mean and variance specifications, and a fat-tailed distribution of the standardised error if normality is rejected. The finite sample properties of the methods and of the algorithm are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods and algorithm are very useful in practice