ANÁLISE DAS MUDANÇAS DOS FATORES DETERMINANTES DA ESTRUTURA DE CAPITAL EM FUNÇÃO DO CICLO DE VIDA DAS EMPRESAS BRASILEIRAS SÃO BERNARDO DO CAMPO

As várias teorias acerca da estrutura de capital despertam interesse motivando diversos estudos sobre o assunto sem, no entanto, ter um consenso. Outro tema aparentemente pouco explorado refere-se ao ciclo de vida das empresas e como ele pode influenciar a estrutura de capital. Este estudo teve como objetivo verificar quais determinantes possuem maior relevância no endividamento das empresas e se estes determinantes alteram-se dependendo do ciclo de vida da empresa apoiada pelas teorias Trade Off, Pecking Order e Teoria da Agência. Para alcançar o objetivo deste trabalho foi utilizado análise em painel de efeito fixo sendo a amostra composta por empresas brasileiras de capital aberto, com dados secundários disponíveis na Economática® no período de 2005 a 2013, utilizando-se os setores da BM&FBOVESPA. Como resultado principal destaca-se o mesmo comportamento entre a amostra geral, alto e baixo crescimento pelo endividamento contábil para o determinante Lucratividade apresentando uma relação negativa, e para os determinantes Oportunidade de Crescimento e Tamanho, estes com uma relação positiva. Para os grupos de alto e baixo crescimento alguns determinantes apresentaram resultados diferentes, como a singularidade que resultou significância nestes dois grupos, sendo positiva no baixo crescimento e negativa no alto crescimento, para o valor colateral dos ativos e benefício fiscal não dívida apresentaram significância apenas no grupo de baixo crescimento. Para o endividamento a valor de mercado foi observado significância para o Benefício fiscal não dívida e Singularidade. Este resultado reforça o argumento de que o ciclo de vida influência a estrutura de capital

Adding constrained discontinuities to Gaussian process models of wind fields

Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.

Adding constrained discontinuities to Gaussian process models of wind fields

Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

Computer interpretation of engineering drawings as solid models

Much of the geometrical data relating to engineering components and assemblies is stored in the form of orthographic views, either on paper or computer files. For various engineering applications, however, it is necessary to describe objects in formal geometric modelling terms. The work reported in this thesis is concerned with the development and implementation of concepts and algorithms for the automatic interpretation of orthographic views as solid models. The various rules and conventions associated with engineering drawings are reviewed and several geometric modelling representations are briefly examined. A review of existing techniques for the automatic, and semi-automatic, interpretation of engineering drawings as solid models is given. A new theoretical approach is then presented and discussed. The author shows how the implementation of such an approach for uniform thickness objects may be extended to more general objects by introducing the concept of `approximation models'. Means by which the quality of the transformations is monitored, are also described. Detailed descriptions of the interpretation algorithms and the software package that were developed for this project are given. The process is then illustrated by a number of practical examples. Finally, the thesis concludes that, using the techniques developed, a substantial percentage of drawings of engineering components could be converted into geometric models with a specific degree of accuracy. This degree is indicative of the suitability of the model for a particular application. Further work on important details is required before a commercially acceptable package is produced.

Exploratory data analysis with non-linear and missing data in geochemistry

Exploratory analysis of data seeks to find common patterns to gain insights into the structure and distribution of the data. In geochemistry it is a valuable means to gain insights into the complicated processes making up a petroleum system. Typically linear visualisation methods like principal components analysis, linked plots, or brushing are used. These methods can not directly be employed when dealing with missing data and they struggle to capture global non-linear structures in the data, however they can do so locally. This thesis discusses a complementary approach based on a non-linear probabilistic model. The generative topographic mapping (GTM) enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate more structure than a two dimensional principal components plot. The model can deal with uncertainty, missing data and allows for the exploration of the non-linear structure in the data. In this thesis a novel approach to initialise the GTM with arbitrary projections is developed. This makes it possible to combine GTM with algorithms like Isomap and fit complex non-linear structure like the Swiss-roll. Another novel extension is the incorporation of prior knowledge about the structure of the covariance matrix. This extension greatly enhances the modelling capabilities of the algorithm resulting in better fit to the data and better imputation capabilities for missing data. Additionally an extensive benchmark study of the missing data imputation capabilities of GTM is performed. Further a novel approach, based on missing data, will be introduced to benchmark the fit of probabilistic visualisation algorithms on unlabelled data. Finally the work is complemented by evaluating the algorithms on real-life datasets from geochemical projects.

Practical methods of tracking of nonstationary time series applied to real-world data

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.

Forecasting the UK/US exchange rate with divisia monetary models and neural networks

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia-based models provide more accurate forecasts than Simple Sum-based models provided they are constructed within a nonlinear framework.

Privatisation, corporate control and employment growth:evidence from a panel of large Polish firms, 1996-2002

Using panel data on large Polish firms this paper examines the relationship between corporate control structures, sales growth and the determinants of employment change during the period 1996-2002. We find that privatised and de novo firms are the main drivers of employment growth and that, in the case of de novo firms, it is foreign ownership which underpins the result. Interestingly, we find that being privatised has a positive impact on employment growth but that this impact is concentrated within a range of three to six years after privatisation. In contrast with the findings of earlier literature, we find evidence that there are no systematic differences in employment response to negative sales growth across the ownership categories. On the other hand, employment in state firms is less responsive to positive sales growth. From these combined results we infer that the behaviour of state firms is constrained by both insider rent sharing and binding budget constraints. Consistent with this, we find that privatised companies, three to six years post-privatisation, are the firms for whom employment is most responsive to positive sales growth and as such, offer the best hope for rapid labour market expansion.

Externalities of openness in innovation

Discussion of open innovation has typically stressed the benefits to the individual enterprise from boundary-spanning linkages and improved internal knowledge sharing. In this paper we explore the potential for wider benefits from openness in innovation and argue that openness may itself generate positive externalities by enabling improved knowledge diffusion. The potential for these (positive) externalities suggests a divergence between the private and social returns to openness and the potential for a sub-optimal level of openness where this is determined purely by firms' private returns. Our analysis is based on Irish plant-level panel data from manufacturing industry over the period 1994-2008. Based on instrumental variables regression models our results suggest that externalities of openness in innovation are significant and that they are positively associated with firms' innovation performance. We find that these externality effects are unlikely to work through their effect on the spread of open innovation practices. Instead, they appear to positively influence innovation outputs by either increasing knowledge diffusion or strengthening competition. Our evidence on the significance of externalities from openness in innovation provides a rationale for public policy aimed at promoting open innovation practices among firms. © 2013 Elsevier B.V. All rights reserved.

A stepwise fuzzy linear programming model with possibility and necessity relations

Linear programming (LP) is the most widely used optimization technique for solving real-life problems because of its simplicity and efficiency. Although conventional LP models require precise data, managers and decision makers dealing with real-world optimization problems often do not have access to exact values. Fuzzy sets have been used in the fuzzy LP (FLP) problems to deal with the imprecise data in the decision variables, objective function and/or the constraints. The imprecisions in the FLP problems could be related to (1) the decision variables; (2) the coefficients of the decision variables in the objective function; (3) the coefficients of the decision variables in the constraints; (4) the right-hand-side of the constraints; or (5) all of these parameters. In this paper, we develop a new stepwise FLP model where fuzzy numbers are considered for the coefficients of the decision variables in the objective function, the coefficients of the decision variables in the constraints and the right-hand-side of the constraints. In the first step, we use the possibility and necessity relations for fuzzy constraints without considering the fuzzy objective function. In the subsequent step, we extend our method to the fuzzy objective function. We use two numerical examples from the FLP literature for comparison purposes and to demonstrate the applicability of the proposed method and the computational efficiency of the procedures and algorithms. © 2013-IOS Press and the authors. All rights reserved.

Advances in data envelopment analysis

Since its introduction in 1978, data envelopment analysis (DEA) has become one of the preeminent nonparametric methods for measuring efficiency and productivity of decision making units (DMUs). Charnes et al. (1978) provided the original DEA constant returns to scale (CRS) model, later extended to variable returns to scale (VRS) by Banker et al. (1984). These ‘standard’ models are known by the acronyms CCR and BCC, respectively, and are now employed routinely in areas that range from assessment of public sectors, such as hospitals and health care systems, schools, and universities, to private sectors, such as banks and financial institutions (Emrouznejad et al. 2008; Emrouznejad and De Witte 2010). The main objective of this volume is to publish original studies that are beyond the two standard CCR and BCC models with both theoretical and practical applications using advanced models in DEA.

Optimal design for correlated processes with input-dependent noise

Optimal design for parameter estimation in Gaussian process regression models with input-dependent noise is examined. The motivation stems from the area of computer experiments, where computationally demanding simulators are approximated using Gaussian process emulators to act as statistical surrogates. In the case of stochastic simulators, which produce a random output for a given set of model inputs, repeated evaluations are useful, supporting the use of replicate observations in the experimental design. The findings are also applicable to the wider context of experimental design for Gaussian process regression and kriging. Designs are proposed with the aim of minimising the variance of the Gaussian process parameter estimates. A heteroscedastic Gaussian process model is presented which allows for an experimental design technique based on an extension of Fisher information to heteroscedastic models. It is empirically shown that the error of the approximation of the parameter variance by the inverse of the Fisher information is reduced as the number of replicated points is increased. Through a series of simulation experiments on both synthetic data and a systems biology stochastic simulator, optimal designs with replicate observations are shown to outperform space-filling designs both with and without replicate observations. Guidance is provided on best practice for optimal experimental design for stochastic response models. © 2013 Elsevier Inc. All rights reserved.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.