Why do firms opt for Alternative-Format Financial Statements ? Some Evidence from France

Historically, the format of financial statements has varied from one country to another. Recently, due to the attractiveness of their capital markets, the strength of their accounting professions and the influence of their institutional investors, Anglo-American countries have seen the impact of their accounting practices on other nations increase steadily, even influencing the actual format of financial statements. Given that French accounting regulations allow a certain degree of choice in consolidated balance sheet format (‘by nature’ or ‘by term’) and income statement format (‘by nature’ or ‘by function’), this study examines a sample of 199 large French listed firms in an attempt to understand why some of these firms do not use the traditional French formats (‘by nature’ for the balance sheet and ‘by nature” for the income statement), instead preferring Anglo-American practices (‘by term’ format for the balance sheet and ‘by function’ format for the income statement). We first analyze the balance sheet and income statement formats separately using a logit model, then combine the two and enrich the research design with a generalized ordered logit model and a multinomial logit regression. Our results confirm that the major driving factor behind the adoption of one or two alternative formats is the firm’s degree of internationalization, not only financial (auditor type, foreign listing and the decision to apply alternative accounting standards) but also commercial (company size and the internationalization of sales).Disclosure; Determinants; Financial Statements; Alternative format; France; Logit; Generalized ordered logit; Multinomial logit

The performance of German firms in the business-related service sectors : a dynamic analysis

We analyze the performance of firms in the German business-related services sector. A quarterly business survey provides the panel data base of our study. Firm performance is measured by the survey respondents? ordinal indication of their changes in total sales. We use a firstorder Markov chain and a multinomial logit specification to model the transition probabilitites. Three variants of the model are estimated: a linear index model with and without unobserved firm heterogeneity and a semiparametric model. Main results are that firm size has a positive effect on firm performance, that young firms outperform older competitors, that a bank-relationship with a single creditor has a stabilizing effect and that the degree of diversification has a negative impact on firm performance. The legal status appears to have no significant effect. --Markov chain,service sector,business survey,firm performance,multinomial logit model,generalized additive model

Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints

In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category. When parameters are penalized, shrinkage of estimates should not depend on the reference category. In this paper we investigate ridge regression for the multinomial logit model with symmetric side constraints, which yields parameter estimates that are independent of the reference category. In simulation studies the results are compared with the usual maximum likelihood estimates and an application to real data is given

Categorical data analysis using a skewed Weibull regression model

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in details. The analysis of two data sets to show the efficiency of the proposed model is performed

The VGAM Package for Categorical Data Analysis

Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such as reduced-rank VGLMs for dimension reduction, and allowing covariates that have values specific to each linear/additive predictor, e.g., for consumer choice modeling. This article describes some of the framework behind the VGAM R package, its usage and implementation details.

A comparison of generalized multinomial logit, random parameters logit, wtp-space and latent class models to studying consumers' preferences for animal welfare

The European societies are requiring that animals to be raised as closely as possible to their natural conditions. The growing concerns about animal welfare is resulting in continuous modifications of regulations and policies that led to ban of a number of intensive farming methods. The European authorities consider the pig welfare as a priority issue. They are studying to ban surgical pig castration by 2018, which may seriously affect markets and consumers due to boar tainted-meat. This study analysed consumers’ preferences and acceptance regarding an alternative to castration of high-level boar-taint frankfurter sausages. Non-hypothetical discrete choice experiments was applied by creating a real shopping scenario before and after tasting the products. Data were collected for a sample of 150 consumers from the metropolitan area of Madrid, Spain. Different modelling approaches (Generalized Multinomial Logit-GMNL, Random Parameters Logit-RPL, WTP-space and Latent Class-LC models) were applied to figure out which model have the best goodness of fit. Results showed the appropriateness of the proposed alternative by using a new flavour as a masking strategy. When consumers tasted the products, they showed their willingness to pay a premium for this flavour. The WTP space model showed the best goodness of fit in terms of likelihood, Akaike information criterion and McFadden Pseudo R2. Furthermore, the degree of randomness identified by the scale parameter is also estimated. Uncertainty in selection decreased significantly after the sensory experiencePostprint (published version

Transport user benefits calculation with the “Rule of a Half” for travel demand models with constraints

The importance of user benefits in transport projects assessments is well-known by transport planners and economists. Generally they have the greatest impact on the result of costbenefit analysis. It is common practice to adopt the consumer surplus measure for calculating transport user benefits. Normally the well-known “Rule of a Half”, as a practical approximation for the integral of the demand curve, is used to determine the change of consumer surplus. In this paper we enter into the question of whether the Rule of a Half is valid in the case of travel demand models with multiple constraints. Such models are often used for travel demand modeling of large-scale areas. The most discussed and well-known model in transport modeling field is the doubly constrained gravity model. Beside this model with inelastic constraints there are also more flexible models with elastic constraints. The theoretical analysis in this paper provides a mathematical proof for the validity of the concept of the Rule of a Half for travel demand models with multiple elastic and inelastic constraints. In this case the Rule of a Half is also a correct approximation of the change of consumer surplus

Partitioned conditional generalized linear models for categorical data

In categorical data analysis, several regression models have been proposed for hierarchically-structured response variables, e.g. the nested logit model. But they have been formally defined for only two or three levels in the hierarchy. Here, we introduce the class of partitioned conditional generalized linear models (PCGLMs) defined for any numbers of levels. The hierarchical structure of these models is fully specified by a partition tree of categories. Using the genericity of the (r,F,Z) specification, the PCGLM can handle nominal, ordinal but also partially-ordered response variables.Comment: 25 pages, 13 figure

Regularized Ordinal Regression and the ordinalNet R Package

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, for instance to accommodate unordered categorical data. We introduce an elastic net penalty class that applies to both model forms. Additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class