Ensaios sobre o poder judiciário no Brasil

A tese apresenta três ensaios empíricos sobre os padrões decisórios de magistrados no Brasil, elaborados à partir de bases de dados inéditas e de larga escala, que contém detalhes de dezenas de milhares de processos judiciais na primeira e na segunda instância. As bases de dados são coletadas pelo próprio autor através de programas-robô de coleta em massa de informações, aplicados aos "links" de acompanhamento processual de tribunais estaduais no Brasil (Paraná, Minas Gerais e Santa Catarina). O primeiro artigo avalia - com base em modelo estatístico - a importância de fatores extra-legais sobre os resultados de ações judiciais, na Justiça Estadual do Paraná. Isto é, se os juízes favorecem sistematicamente a parte hipossuficiente (beneficiária de Assistência Judiciária Gratuita). No segundo artigo, estuda-se a relação entre a duração de ações cíveis no primeiro grau e a probabilidade de reforma da sentença, utilizando-se dados da Justiça Estadual de Minas Gerais. O objetivo é avaliar se existe um dilema entre a duração e a qualidade das sentenças. Dito de outra forma, se existe um dilema entre a observância do direito ao devido processo legal e a celeridade processual. O último artigo teste a hipótese - no âmbito de apelações criminais e incidentes recursais no Tribunal de Justiça de Santa Catarina - de que as origens profissionais dos desembargadores influenciam seus padrões decisórios. Isto é, testa-se a hipótese de que desembargadores/relatores oriundos da carreira da advocacia são mais "garantistas" ( e desembargadores oriundos da carreira do Ministério Público são menos "garantistas") relativamente aos seus pares oriundos da carreira da magistratura. Testam-se as hipóteses com base em um modelo estatístico que explica a probabilidade de uma decisão recursal favorável ao réu, em função da origem de carreira do relator do recurso, além de um conjunto de características do processo e do órgão julgador.

A monthly indicator of Brazilian GDP. The Brazilian business cycle and growth cycle

This paper constructs an indicator of Brazilian GDP at the monthly ftequency. The peculiar instability and abrupt changes of regimes in the dynamic behavior of the Brazilian business cycle were explicitly modeled within nonlinear ftameworks. In particular, a Markov switching dynarnic factor model was used to combine several macroeconomic variables that display simultaneous comovements with aggregate economic activity. The model generates as output a monthly indicator of the Brazilian GDP and real time probabilities of the current phase of the Brazilian business cycle. The monthly indicator shows a remarkable historical conformity with cyclical movements of GDP. In addition, the estimated filtered probabilities predict ali recessions in sample and out-of-sample. The ability of the indicator in linear forecasting growth rates of GDP is also examined. The estimated indicator displays a better in-sample and out-of-sample predictive performance in forecasting growth rates of real GDP, compared to a linear autoregressive model for GDP. These results suggest that the estimated monthly indicator can be used to forecast GDP and to monitor the state of the Brazilian economy in real time.

Forecast comparison with nonlinear methods for brazilian industrial production

Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.

Análise da precificação de imóveis na cidade do Rio de Janeiro utilizando Modelagem Hedônica e os efeitos da autocorrelação espacial

A escolha da cidade do Rio de Janeiro como sede de grandes eventos esportivos mundiais, a Copa do Mundo de Futebol de 2014 e os Jogos Olímpicos de 2016, colocou-a no centro de investimentos em infraestrutura, mobilidade urbana e segurança pública, com consequente impacto no mercado imobiliário, tanto de novos lançamentos de empreendimentos, quanto na revenda de imóveis usados. Acredita-se que o preço de um imóvel dependa de uma relação entre suas características estruturais como quantidade de quartos, suítes, vagas de garagem, presença de varanda, tal como sua localização, proximidade com centros de trabalho, entretenimento e áreas valorizadas ou degradadas. Uma das técnicas para avaliar a contribuição dessas características para a formação do preço do imóvel, conhecido na Econométrica como Modelagem Hedônica de Preços, é uma aplicação de regressão linear multivariada onde a variável dependente é o preço e as variáveis independentes, as respectivas características que deseja-se modelar. A utilização da regressão linear implica em observar premissas que devem ser atendidas para a confiabilidade dos resultados a serem analisados, tais como independência e homoscedasticidade dos resíduos e não colinearidade entre as variáveis independentes. O presente trabalho objetiva aplicar a modelagem hedônica de preços para imóveis localizados na cidade do Rio de Janeiro em um modelo de regressão linear multivariada, em conjunto com outras fontes de dados para a construção de variáveis de acessibilidade e socioambiental a fim de verificar a relação de importância entre elas para a formação do preço e, em particular, exploramos brevemente a tendência de preços em função da distância a favelas. Em atenção aos pré-requisitos observados para a aplicação de regressão linear, verificamos que a premissa de independência dos preços não pode ser atestada devido a constatação da autocorrelação espacial entre os imóveis, onde não apenas as características estruturais e de acessibilidade são levadas em consideração para a precificação do bem, mas principalmente a influência mútua que os imóveis vizinhos exercem um ao outro.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).

Sistema inteligente para estimar a porosidade em sedimentos a partir da análise de sinais GPR

This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)

Câncer de boca e orofaringe: tendências e análise de sobrevida em Natal (RN)

Introduction: Mouth cancer is classified as having one of the ten highest cancer incidences in the world. In Brazil, the incidence and mortality rates of oral cancer are among the highest in the world. Intraoral cancer (tongue, gum, floor of the mouth, and other non-specified parts of the mouth), the accumulated survival rate after five years is less than 50%. Objectives: Estimate the accumulated survival probability after five years and adjust the Cox regression model for mouth and oropharyngeal cancers, according to age range, sex, morphology, and location, for the city of Natal. Describe the mortality and incidence coefficients of oral and oropharyngeal cancer and their tendencies in the city of Natal, between 1980 and 2001 and between 1997 and 2001, respectively. Methods: Survival data of patients registered between 1997 and 2001 was obtained from the Population-based Cancer Record of Natal. Differences between the survival curves were tested using the log-rank test. The Cox proportional risk model was used to estimate risk ratios. The simple linear regression model was used for tendency analyses of the mortality and incidence coefficients. Results: The probability after five years was 22.9%. The patients with undifferentiated malignant neoplasia were 4.7 times more at risk of dying than those with epidermoid carcinoma, whereas the patients with oropharyngeal cancer had 2.0 times more at risk of dying than those with mouth cancer. The mouth cancer mortality and incidence coefficients for Natal were 4.3 and 2.9 per 100 000 inhabitants, respectively. The oropharyngeal cancer mortality and incidence coefficients were, respectively, 1.1 and 0.7 per 100 000 87 inhabitants. Conclusions: A low survival rate after five years was identified. Patients with oropharyngeal cancer had a greater risk of dying, independent of the factors considered in this study. Also independent of other factors, undifferentiated malignant neoplasia posed a greater risk of death. The magnitudes of the incidence coefficients found are not considered elevated, whereas the magnitudes of the mortality coefficients are high

Estimativa da área foliar de Ageratum conyzoides usando dimensões lineares do limbo foliar

The aim of this research was to obtain a mathematical equation to estimate the leaf area of Ageratum conyzoides based on linear measures of its leaf blade. Correlation studies were done using real leaf area (Sf), leaf length (C) and the maximum leaf width (L), in about 200 leaf blades. The evaluated statistic models were: linear Y = a + bx; simple linear Y = bx; geometric Y = ax(b); and exponential Y = ab(x). The evaluated linear, exponential and geometric models can be used in the billygoat weed leaf area estimation. In the practical sense, the simple linear regression model is suggested using the C*L multiplication product and taking the linear coefficient equal to zero, because it showed weak-alteration on sum of squares error and satisfactory residual analysis. Thus, an estimate of A conyzoides leaf area can be obtained using the equation Sf = 0.6789*(C*L), with a determination coefficient of 0.8630.

Estimativa da área foliar de Sida cordifolia e Sida rhombifolia usando dimensões lineares do limbo foliar

A estimativa da área foliar pode auxiliar na compreensão de relações de interferência entre plantas daninhas e cultivadas. Com o objetivo de obter uma equação que, por meio de parâmetros lineares dimensionais das folhas, permita a estimativa da área foliar de Sida cordifolia e Sida rhombifolia, estudaram-se as correlações entre área foliar real (Af) e parâmetros dimensionais do limbo foliar, como o comprimento (C) ao longo da nervura principal e a largura máxima (L) perpendicular à nervura principal. Foram analisados 200 limbos foliares de cada espécie, coletados em diferentes agroecossistemas na Universidade Estadual Paulista, campus de Jaboticabal. Os modelos estatísticos utilizados foram linear: Y = a + bx; linear simples: Y = bx; geométrico: Y = ax b; e exponencial: Y = ab x. Todos os modelos analisados podem ser empregados para estimação da área foliar de S. cordifolia e S. rhombifolia. Sugere-se optar pela equação linear simples, envolvendo o produto C*L, considerando-se o coeficiente linear igual a zero, em função da praticidade desta. Desse modo, a estimativa da área foliar de S. cordifolia pode ser obtida pela fórmula Af = 0,7878*(C*L), com coeficiente de determinação de 0,9307, enquanto para S. rhombifolia a estimativa da área foliar pode ser obtida pela fórmula Af = 0,6423*(C*L), com coeficiente de determinação de 0,9711.

Mortalidade infantil e condições socioeconômicas nas microrregiões do Nordeste Brasileiro

The study aims to answer the following question: what are the different profiles of infant mortality, according to demographic, socioeconomic, infrastructure and health care, for the micro-regions at the Northeast of Brazil? Thus, the main objective is to analyze the profiles or typologies associated mortality levels sociodemographic conditions of the micro-regions, in the year 2010. To this end, the databases of birth and death certificates of SIM and SINASC (DATASUS/MS), were taken from the 2010 population Census microdata and from SIDRA/IBGE. As a methodology, a weighted multiple linear regression model was used in the analysis in order to find the most significant variables in the explanation child mortality for the year 2010. Also a cluster analysis was performed, seeking evidence, initially, of homogeneous groups of micro-regions, from of the significant variables. The logit of the infant mortality rate was used as dependent variable, while variables such as demographic, socioeconomic, infrastructure and health care in the micro-regions were taken as the independent variables of the model. The Bayesian estimation technique was applied to the database of births and deaths, due to the inconvenient fact of underreporting and random fluctuations of small quantities in small areas. The techniques of Spatial Statistics were used to determine the spatial behavior of the distribution of rates from thematic maps. In conclusion, we used the method GoM (Grade of Membership), to find typologies of mortality, associated with the selected variables by micro-regions, in order to respond the main question of the study. The results points out to the formation of three profiles: Profile 1, high infant mortality and unfavorable social conditions; Profile 2, low infant mortality, with a median social conditions of life; and Profile 3, median and high infant mortality social conditions. With this classification, it was found that, out of 188 micro-regions, 20 (10%) fits the extreme profile 1, 59 (31.4%) was characterized in the extreme profile 2, 34 (18.1%) was characterized in the extreme profile 3 and only 9 (4.8%) was classified as amorphous profile. The other micro-regions framed up in the profiles mixed. Such profiles suggest the need for different interventions in terms of public policies aimed to reducing child mortality in the region

Modelagem numérica de estruturas de contenção atirantadas em areia

A numerical study on the behavior of tied-back retaining walls in sand, using the finite element method (FEM) is presented. The analyses were performed using the software Plaxis 2D, and were focused on the development of horizontal displacements, horizontal stresses, shear forces and bending moments in the structure during the construction process. Emphasis was placed on the evaluation of wall embedment, tie-back horizontal spacing, wall thickness, and free anchor length on wall behavior. A representative soil profile of a specific region at the City of Natal, Brazil, was used in the numerical analyses. New facilities built on this region often include retaining structures of the same type studied herein. Soil behavior was modeled using the Mohr-Coulomb constitutive model, whereas the structural elements were modeled using the linear elastic model. Shear strength parameters of the soil layers were obtained from direct shear test results conducted with samples collected at the studied site. Deformation parameters were obtained from empirical correlations from SPT test results carried out on the studied site. The results of the numerical analyses revealed that the effect of wall embedment on the investigated parameters is virtually negligible. Conversely, the tie-back horizontal spacing plays an important role on the investigated parameters. The results also demonstrated that the wall thickness significantly affects the wall horizontal displacements, and the shear forces and bending moments within the retaining structure. However, wall thickness was not found to influence horizontal stresses in the structure

Dogs can detect scat samples more efficiently than humans: an experiment in a continuous Atlantic Forest remnant

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameters for an alternative criterion to improve productive longevity of Nellore cows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic parameters and relationships of heifer pregnancy and age at first calving with weight gain, yearling and mature weight in Nelore cattle

The objectives of the current study were to investigate the additive genetic associations between heifer pregnancy at 16 months of age (HP16) and age at first calving (AFC) with weight gain from birth to weaning (WG), yearling weight (YW) and mature weight (MW), in order to verify the possibility of using the traits measured directly in females as selection criteria for the genetic improvement of sexual precocity in Nelore cattle. (Co)variance components were estimated by Bayesian inference using a linear animal model for AFC, WG, YW and MW and a nonlinear (threshold) animal model for HP16. The posterior means of direct heritability estimates were: 0.45 +/- 0.02; 0.10 +/- 0.01; 023 +/- 0.02; 0.36 +/- 0.01 and 0.39 +/- 0.04, for HP16, AFC, WG, YW and MW, respectively. Maternal heritability estimate for WG was 0.07 +/- 0.01. Genetic correlations estimated between HP16 and WG, YW and MW were 0.19 +/- 0.04; 0.25 +/- 0.06 and 0.14 +/- 0.05, respectively. The genetic correlations of AFC with WG, YW and MW were low to moderate and negative, with values of -0.18 +/- 0.06; -0.22 +/- 0.05 and -0.12 +/- 0.05, respectively. The high heritability estimated for HP16 suggests that this trait seem to be a better selection criterion for females sexual precocity than AFC. Long-term selection for animals that are heavier at young ages tends to improve the heifers sexual precocity evaluated by HP16 or AFC. Predicted breeding values for HP16 can be used to select bulls and it can lead to an improvement in sexual precocity. The inclusion of HP16 in a selection index will result in small or no response for females mature weight. (C) 2011 Elsevier B.V. All rights reserved.