Corporate Financial Policy, Taxation, and Macroeconomic Risk

This paper develops a simple model of corporate financial structure intended to formalize the macroeconomic concern over excessive leverage. In particular, we attempt to rationalize why firms designing an optimal capital structure would choose a level of debt that leaves them heavily exposed to macroeconomic risk. Our starting point is a variant of the "corporate control" model often used to motivate debt as the optimal financial contract. We modify this framework in two ways. First, we include common risks, interpretable as business cycle risks, as well as idiosyncratic risks. Second, we include corporate and investor-level taxes, and consider the implications of a net tax bias against equity finance. The tax distortion confronts firms with a tradeoff ex ante between the costs of equity finance and the costs of increased exposure to macroeconomic risk accompanying debt finance. In this regard, an equilibrium with "excessive leverage" is possible. Further, despite the possibility of renegotiation, debt is in general less effective than equity in insulating the firm against aggregate risk. Our model leads to the prediction that individual firm dividends may vary with macroeconomic conditions, even after controlling for the effects of relevant firm-specific performance measures, such as earnings. We present some formal econometric evidence in support of this prediction, using a panel of individual corporations. Evidence on some related predictions is also presented.

Interactions of antibody with the capsular polypeptide of Bacillus anthracis

Bacillus anthracis is the etiologic agent of anthrax. The bacterium is surrounded by a capsule entirely assembled from gamma (g)-linked D-glutamic acid (gDPGA). The polymer is produced immediately following spore germination and is a considerable obstacle to the immune system. Previous studies have indicated that monoclonal antibodies (mAbs) reactive with the bacterial capsule are effective reagents for antimicrobial therapy and immunodiagnosis; both applications have tremendous potential in the field of bio-defense. gDPGA is a unique antigen; some of its characteristics include high molecular weight, resistance to degradation, negative charge, and repeating subunits. The work presented in this dissertation was carried out to more fully understand how mAbs interact with gDPGA. We determined that binding may occur in a stereo-selective manner, despite the repetitious character of the polymer. Transfer of a murine IgG3 anti-gDPGA variable region to human IgG1-4 constant domains resulted in severe binding alterations. Affinity maturation of a gDPGA-reactive mAb F26G3 facilitated the assessment of subclass and binding attributes as independent variables. These studies highlighted the importance of capsular reactivity to protection. Finally, the function of murine IgG3-specific post-translational modifications were examined. Deglycosylation altered the fundamental capsular reactivity of murine IgG3. Together, the results indicate that mAb:gDPGA interactions are surprisingly complex. Murine IgG3 anti-gDPGA mAbs retain characteristics not observed in any other murine or human antibody; suggesting a purposeful mechanism to manage encapsulated pathogens

Mineral Processing by Short Circuits in Protoplanetary Disks

Meteoritic chondrules were formed in the early solar system by brief heating of silicate dust to melting temperatures. Some highly refractory grains (Type B calcium-aluminum-rich inclusions, CAIs) also show signs of transient heating. A similar process may occur in other protoplanetary disks, as evidenced by observations of spectra characteristic of crystalline silicates. One possible environment for this process is the turbulent magnetohydrodynamic flow thought to drive accretion in these disks. Such flows generally form thin current sheets, which are sites of magnetic reconnection, and dissipate the magnetic fields amplified by a disk dynamo. We suggest that it is possible to heat precursor grains for chondrules and other high-temperature minerals in current sheets that have been concentrated by our recently described short-circuit instability. We extend our work on this process by including the effects of radiative cooling, taking into account the temperature dependence of the opacity; and by examining current sheet geometry in three-dimensional, global models of magnetorotational instability. We find that temperatures above 1600 K can be reached for favorable parameters that match the ideal global models. This mechanism could provide an efficient means of tapping the gravitational potential energy of the protoplanetary disk to heat grains strongly enough to form high-temperature minerals. The volume-filling nature of turbulent magnetic reconnection is compatible with constraints from chondrule-matrix complementarity, chondrule-chondrule complementarity, the occurrence of igneous rims, and compound chondrules. The same short-circuit mechanism may perform other high-temperature mineral processing in protoplanetary disks such as the production of crystalline silicates and CAIs.Comment: 6 pages, 3 figures, ApJL published versio

Implications of Shock Wave Experiments with Precompressed Materials for Giant Planet Interiors

This work uses density functional molecular dynamics simulations of fluid helium at high pressure to examine how shock wave experiments with precompressed samples can help characterizing the interior of giant planets. In particular, we analyze how large of a precompression is needed to probe a certain depth in a planet's gas envelope. We find that precompressions of up to 0.1, 1.0, 10, or 100 GPa are needed to characterized 2.5, 5.9, 18, to 63% of Jupiter's envelope by mass.Comment: Submitted As Proceedings Article For The American Physical Society Meeting On Shock Compression Of Condensed Matter, Hawaii, June, 200

Population Intervention Models in Causal Inference

Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a] treatment variable or risk variable on the distribution of a disease in a population. These models, as originally introduced by Robins (e.g., Robins (2000a), Robins (2000b), van der Laan and Robins (2002)), model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates, and its dependence on treatment. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject\u27s treatment and covariate history are measured over time, and an outcome is recorded at a final time point. In addition to the simpler, weighted regression approaches (inverse probability of treatment weighted estimators), more general (and robust) estimators have been developed and studied in detail for standard MSM (Robins (2000b), Neugebauer and van der Laan (2004), Yu and van der Laan (2003), van der Laan and Robins (2002)). In this paper we argue that in many applications one is interested in modeling the difference between a treatment-specific counterfactual population distribution and the actual population distribution of the target population of interest. Relevant parameters describe the effect of a hypothetical intervention on such a population, and therefore we refer to these models as intervention models. We focus on intervention models estimating the effect on an intervention in terms of a difference of means, ratio in means (e.g., relative risk if the outcome is binary), a so called switch relative risk for binary outcomes, and difference in entire distributions as measured by the quantile-quantile function. In addition, we provide a class of inverse probability of treatment weighed estimators, and double robust estimators of the causal parameters in these models. We illustrate the finite sample performance of these new estimators in a simulation study

Nonparametric population average models: deriving the form of approximate population average models estimated using generalized estimating equations

For estimating regressions for repeated measures outcome data, a popular choice is the population average models estimated by generalized estimating equations (GEE). We review in this report the derivation of the robust inference (sandwich-type estimator of the standard error). In addition, we present formally how the approximation of a misspecified working population average model relates to the true model and in turn how to interpret the results of such a misspecified model