The Impact of Personal and Corporate Taxation on Capital Structure Choices

This paper empirically analyses whether both personal and corporate taxation have an impact on companies' capital structure decisions. We investigate the effect of the difference in taxation of debt and equity financing on capital structures. Our empirical results, based on a comprehensive panel of European firm-level data, suggest that a higher tax benefit of debt has the expected significant positive impact on a company's financial leverage. Particularly, we find evidence that the capital structures of smaller companies respond more heavily to changes in the tax benefit of debt. Additional analysis confirms that not only corporate taxes are relevant for corporate financial planning, but variation in capital income tax rates at the shareholder level implicates significant capital structure adjustments as well. Moreover, we find substitutive relationships between non-debt tax shields and the effect of the corporate tax rate on capital structures. --Capital Structure,Corporate Income Tax,Personal Income Tax,Firm-Level Data

Additions and Corrections to the Stoneflies (Plecoptera) of Iowa, U.S.A.

(exerpt) Until recently, Iowa’s stonefly fauna was poorly documented. Heimdal et al. (2004) published a comprehensive report on stonefly distributions within the state, reporting seven families and 43 species. Five species, Allocapnia pygmaea (Burmeister) (Capiniidae), Leuctra tenuis (Pictet) (Leuctridae), Amphinemura linda (Ricker) (Nemouridae), Nemoura trispinosa Claassen (Nemouridae), and Soyedina vallicularia (Wu) (Nemouridae), were recommended for state protection because of their limited distribution within Iowa. Four species, Amphinemura delosa (Ricker), Isogenoides doratus (Frison) (Perlodidae), I. krumholzi (Ricker), and I. varians (Walsh), had limited distributions, but were not listed because their observed habitat preferences appeared common or were difficult to sample and poorly collected. From 2004 to 2006, fifteen county, state, and federal parks and preserves in east and northeast Iowa were sampled during the spring and summer in an effort to find additional locations for these nine species. The surveys yielded new distributional data for five species, including two new state records, and one species deletion, updating the total number of species recorded from Iowa to 44. A discussion for these records and corrections is presented below. Material collected from these surveys was deposited in the University of Iowa Hygienic Laboratory Collection (UHL) and the Illinois Natural History Survey Insect Collection (INHS)

Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation

The utility of knowledge in inductive learning

In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constant-free Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a rule and on the accuracy of a learned rule. Moreover, we demonstrate that a hybrid explanation-based and inductive learning method can advantageously use an approximate domain theory, even when this theory is incorrect and incomplete

Indirect likelihood inference

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.indirect inference; maximum-likelihood; simulation-based

SNM Guide

This is a guide that explains how to use software that implements the simulated nonparametric moments (SNM) estimator proposed by Creel and Kristensen (2009). The guide shows how results of that paper may easily be replicated, and explains how to install and use the software for estimation of simulable econometric models.econometric software; dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation

Safe Harbors for Quantity Discounts and Bundling

The courts and analysts continue to struggle to articulate safe harbors for a wide variety of common business pricing practices in which either a single product is sold at a discount if purchased in bulk or in which multiple products are bundled together at prices different from the ones that would emerge if the products were purchased separately. The phenomenon of tying in which the sale of one product is conditioned on the purchase of another is closely related to bundling. Its analysis relies on the same economics as that used to analyze bundling (see, e.g., Carlton and Waldman (2008)), though the law seems to make a distinction between the two. The need for safe harbors for common business pricing practices arises from the recognition that these practices often are motivated by efficiency and that a broad antitrust attack on them could cause more harm than good. In this essay, we analyze and propose safe harbors for quantity discounts and bundled products. In analyzing the latter case, we discuss the deficiencies of the particular safe harbor proposed in the report of the Antitrust Modernization Commission (2007) (AMC) of which Carlton was a member.Tying, Bundling, Safe Harbor, Antitrust