De lo micro a lo macro prudencial: Un análisis de la supervisión bancaria desde la econometría espacial

La crisis que se desató en el mercado hipotecario en Estados Unidos en 2008 y que logró propagarse a lo largo de todo sistema financiero, dejó en evidencia el nivel de interconexión que actualmente existe entre las entidades del sector y sus relaciones con el sector productivo, dejando en evidencia la necesidad de identificar y caracterizar el riesgo sistémico inherente al sistema, para que de esta forma las entidades reguladoras busquen una estabilidad tanto individual, como del sistema en general. El presente documento muestra, a través de un modelo que combina el poder informativo de las redes y su adecuación a un modelo espacial auto regresivo (tipo panel), la importancia de incorporar al enfoque micro-prudencial (propuesto en Basilea II), una variable que capture el efecto de estar conectado con otras entidades, realizando así un análisis macro-prudencial (propuesto en Basilea III).

Behavioral differences in violence: the case of intra-group differences of paramilitaries and guerrillas in Colombia

In most studies on civil wars, determinants of conflict have been hitherto explored assuming that actors involved were either unitary or stable. However, if this intra-group homogeneity assumption does not hold, empirical econometric estimates may be biased. We use Fixed Effects Finite Mixture Model (FE-FMM) approach to address this issue that provides a representation of heterogeneity when data originate from different latent classes and the affiliation is unknown. It allows to identify sub-populations within a population as well as the determinants of their behaviors. By combining various data sources for the period 2000-2005, we apply this methodology to the Colombian conflict. Our results highlight a behavioral heterogeneity in guerrilla’s armed groups and their distinct economic correlates. By contrast paramilitaries behave as a rather homogenous group.

Network externalities across financial institutions

We propose and estimate a financial distress model that explicitly accounts for the interactions or spill-over effects between financial institutions, through the use of a spatial continuity matrix that is build from financial network data of inter bank transactions. Such setup of the financial distress model allows for the empirical validation of the importance of network externalities in determining financial distress, in addition to institution specific and macroeconomic covariates. The relevance of such specification is that it incorporates simultaneously micro-prudential factors (Basel 2) as well as macro-prudential and systemic factors (Basel 3) as determinants of financial distress. Results indicate network externalities are an important determinant of financial health of a financial institutions. The parameter that measures the effect of network externalities is both economically and statistical significant and its inclusion as a risk factor reduces the importance of the firm specific variables such as the size or degree of leverage of the financial institution. In addition we analyze the policy implications of the network factor model for capital requirements and deposit insurance pricing.

Forest Policy Scenario Analysis: Sensitivity of Songbird Community to Changes in Forest Cover Amount and Configuration

Changes in mature forest cover amount, composition, and configuration can be of significant consequence to wildlife populations. The response of wildlife to forest patterns is of concern to forest managers because it lies at the heart of such competing approaches to forest planning as aggregated vs. dispersed harvest block layouts. In this study, we developed a species assessment framework to evaluate the outcomes of forest management scenarios on biodiversity conservation objectives. Scenarios were assessed in the context of a broad range of forest structures and patterns that would be expected to occur under natural disturbance and succession processes. Spatial habitat models were used to predict the effects of varying degrees of mature forest cover amount, composition, and configuration on habitat occupancy for a set of 13 focal songbird species. We used a spatially explicit harvest scheduling program to model forest management options and simulate future forest conditions resulting from alternative forest management scenarios, and used a process-based fire-simulation model to simulate future forest conditions resulting from natural wildfire disturbance. Spatial pattern signatures were derived for both habitat occupancy and forest conditions, and these were placed in the context of the simulated range of natural variation. Strategic policy analyses were set in the context of current Ontario forest management policies. This included use of sequential time-restricted harvest blocks (created for Woodland caribou (Rangifer tarandus) conservation) and delayed harvest areas (created for American marten (Martes americana atrata) conservation). This approach increased the realism of the analysis, but reduced the generality of interpretations. We found that forest management options that create linear strips of old forest deviate the most from simulated natural patterns, and had the greatest negative effects on habitat occupancy, whereas policy options that specify deferment and timing of harvest for large blocks helped ensure the stable presence of an intact mature forest matrix over time. The management scenario that focused on maintaining compositional targets best supported biodiversity objectives by providing the composition patterns required by the 13 focal species, but this scenario may be improved by adding some broad-scale spatial objectives to better maintain large blocks of interior forest habitat through time.

Modeling and Mapping Golden-winged Warbler Abundance to Improve Regional Conservation Strategies

Conservation planning requires identifying pertinent habitat factors and locating geographic locations where land management may improve habitat conditions for high priority species. I derived habitat models and mapped predicted abundance for the Golden-winged Warbler (Vermivora chrysoptera), a species of high conservation concern, using bird counts, environmental variables, and hierarchical models applied at multiple spatial scales. My aim was to understand habitat associations at multiple spatial scales and create a predictive abundance map for purposes of conservation planning for the Golden-winged Warbler. My models indicated a substantial influence of landscape conditions, including strong positive associations with total forest composition within the landscape. However, many of the associations I observed were counter to reported associations at finer spatial extents; for instance, I found Golden-winged Warblers negatively associated with several measures of edge habitat. No single spatial scale dominated, indicating that this species is responding to factors at multiple spatial scales. I found Golden-winged Warbler abundance was negatively related with Blue-winged Warbler (Vermivora cyanoptera) abundance. I also observed a north-south spatial trend suggestive of a regional climate effect that was not previously noted for this species. The map of predicted abundance indicated a large area of concentrated abundance in west-central Wisconsin, with smaller areas of high abundance along the northern periphery of the Prairie Hardwood Transition. This map of predicted abundance compared favorably with independent evaluation data sets and can thus be used to inform regional planning efforts devoted to conserving this species.

Preferred structures in large-scale circulation and the effect of doubling greenhouse gas concentration in HadCM3

Preferred structures in the surface pressure variability are investigated in and compared between two 100-year simulations of the Hadley Centre climate model HadCM3. In the first (control) simulation, the model is forced with pre-industrial carbon dioxide concentration (1×CO2) and in the second simulation the model is forced with doubled CO2 concentration (2×CO2). Daily winter (December-January-February) surface pressures over the Northern Hemisphere are analysed. The identification of preferred patterns is addressed using multivariate mixture models. For the control simulation, two significant flow regimes are obtained at 5% and 2.5% significance levels within the state space spanned by the leading two principal components. They show a high pressure centre over the North Pacific/Aleutian Islands associated with a low pressure centre over the North Atlantic, and its reverse. For the 2×CO2 simulation, no such behaviour is obtained. At higher-dimensional state space, flow patterns are obtained from both simulations. They are found to be significant at the 1% level for the control simulation and at the 2.5% level for the 2×CO2 simulation. Hence under CO2 doubling, regime behaviour in the large-scale wave dynamics weakens. Doubling greenhouse gas concentration affects both the frequency of occurrence of regimes and also the pattern structures. The less frequent regime becomes amplified and the more frequent regime weakens. The largest change is observed over the Pacific where a significant deepening of the Aleutian low is obtained under CO2 doubling.

Asymptotic normality in mixtures of power series distributions

The problem of estimating the individual probabilities of a discrete distribution is considered. The true distribution of the independent observations is a mixture of a family of power series distributions. First, we ensure identifiability of the mixing distribution assuming mild conditions. Next, the mixing distribution is estimated by non-parametric maximum likelihood and an estimator for individual probabilities is obtained from the corresponding marginal mixture density. We establish asymptotic normality for the estimator of individual probabilities by showing that, under certain conditions, the difference between this estimator and the empirical proportions is asymptotically negligible. Our framework includes Poisson, negative binomial and logarithmic series as well as binomial mixture models. Simulations highlight the benefit in achieving normality when using the proposed marginal mixture density approach instead of the empirical one, especially for small sample sizes and/or when interest is in the tail areas. A real data example is given to illustrate the use of the methodology.

Modelling heterotachy in phylogenetic inference by reversible-jump Markov chain Monte Carlo

The rate at which a given site in a gene sequence alignment evolves over time may vary. This phenomenon-known as heterotachy-can bias or distort phylogenetic trees inferred from models of sequence evolution that assume rates of evolution are constant. Here, we describe a phylogenetic mixture model designed to accommodate heterotachy. The method sums the likelihood of the data at each site over more than one set of branch lengths on the same tree topology. A branch-length set that is best for one site may differ from the branch-length set that is best for some other site, thereby allowing different sites to have different rates of change throughout the tree. Because rate variation may not be present in all branches, we use a reversible-jump Markov chain Monte Carlo algorithm to identify those branches in which reliable amounts of heterotachy occur. We implement the method in combination with our 'pattern-heterogeneity' mixture model, applying it to simulated data and five published datasets. We find that complex evolutionary signals of heterotachy are routinely present over and above variation in the rate or pattern of evolution across sites, that the reversible-jump method requires far fewer parameters than conventional mixture models to describe it, and serves to identify the regions of the tree in which heterotachy is most pronounced. The reversible-jump procedure also removes the need for a posteriori tests of 'significance' such as the Akaike or Bayesian information criterion tests, or Bayes factors. Heterotachy has important consequences for the correct reconstruction of phylogenies as well as for tests of hypotheses that rely on accurate branch-length information. These include molecular clocks, analyses of tempo and mode of evolution, comparative studies and ancestral state reconstruction. The model is available from the authors' website, and can be used for the analysis of both nucleotide and morphological data.

Infant mortality model for lifetime data

In this paper we introduce a parametric model for handling lifetime data where an early lifetime can be related to the infant-mortality failure or to the wear processes but we do not know which risk is responsible for the failure. The maximum likelihood approach and the sampling-based approach are used to get the inferences of interest. Some special cases of the proposed model are studied via Monte Carlo methods for size and power of hypothesis tests. To illustrate the proposed methodology, we introduce an example consisting of a real data set.

Bayesian density estimation using Skew Student-t-Normal mixtures

We present a Bayesian approach for modeling heterogeneous data and estimate multimodal densities using mixtures of Skew Student-t-Normal distributions [Gomez, H.W., Venegas, O., Bolfarine, H., 2007. Skew-symmetric distributions generated by the distribution function of the normal distribution. Environmetrics 18, 395-407]. A stochastic representation that is useful for implementing a MCMC-type algorithm and results about existence of posterior moments are obtained. Marginal likelihood approximations are obtained, in order to compare mixture models with different number of component densities. Data sets concerning the Gross Domestic Product per capita (Human Development Report) and body mass index (National Health and Nutrition Examination Survey), previously studied in the related literature, are analyzed. (c) 2008 Elsevier B.V. All rights reserved.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Three essays on local demand for public services

Local provision of public services has the positive effect of increasing the efficiency because each locality has its idiosyncrasies that determine a particular demand for public services. This dissertation addresses different aspects of the local demand for public goods and services and their relationship with political incentives. The text is divided in three essays. The first essay aims to test the existence of yardstick competition in education spending using panel data from Brazilian municipalities. The essay estimates two-regime spatial Durbin models with time and spatial fixed effects using maximum likelihood, where the regimes represent different electoral and educational accountability institutional settings. First, it is investigated whether the lame duck incumbents tend to engage in less strategic interaction as a result of the impossibility of reelection, which lowers the incentives for them to signal their type (good or bad) to the voters by mimicking their neighbors’ expenditures. Additionally, it is evaluated whether the lack of electorate support faced by the minority governments causes the incumbents to mimic the neighbors’ spending to a greater extent to increase their odds of reelection. Next, the essay estimates the effects of the institutional change introduced by the disclosure on April 2007 of the Basic Education Development Index (known as IDEB) and its goals on the strategic interaction at the municipality level. This institutional change potentially increased the incentives for incumbents to follow the national best practices in an attempt to signal their type to voters, thus reducing the importance of local information spillover. The same model is also tested using school inputs that are believed to improve students’ performance in place of education spending. The results show evidence for yardstick competition in education spending. Spatial auto-correlation is lower among the lame ducks and higher among the incumbents with minority support (a smaller vote margin). In addition, the institutional change introduced by the IDEB reduced the spatial interaction in education spending and input-setting, thus diminishing the importance of local information spillover. The second essay investigates the role played by the geographic distance between the poor and non-poor in the local demand for income redistribution. In particular, the study provides an empirical test of the geographically limited altruism model proposed in Pauly (1973), incorporating the possibility of participation costs associated with the provision of transfers (Van de Wale, 1998). First, the discussion is motivated by allowing for an “iceberg cost” of participation in the programs for the poor individuals in Pauly’s original model. Next, using data from the 2000 Brazilian Census and a panel of municipalities based on the National Household Sample Survey (PNAD) from 2001 to 2007, all the distance-related explanatory variables indicate that an increased proximity between poor and non-poor is associated with better targeting of the programs (demand for redistribution). For instance, a 1-hour increase in the time spent commuting by the poor reduces the targeting by 3.158 percentage points. This result is similar to that of Ashworth, Heyndels and Smolders (2002) but is definitely not due to the program leakages. To empirically disentangle participation costs and spatially restricted altruism effects, an additional test is conducted using unique panel data based on the 2004 and 2006 PNAD, which assess the number of benefits and the average benefit value received by beneficiaries. The estimates suggest that both cost and altruism play important roles in targeting determination in Brazil, and thus, in the determination of the demand for redistribution. Lastly, the results indicate that ‘size matters’; i.e., the budget for redistribution has a positive impact on targeting. The third essay aims to empirically test the validity of the median voter model for the Brazilian case. Information on municipalities are obtained from the Population Census and the Brazilian Supreme Electoral Court for the year 2000. First, the median voter demand for local public services is estimated. The bundles of services offered by reelection candidates are identified as the expenditures realized during incumbents’ first term in office. The assumption of perfect information of candidates concerning the median demand is relaxed and a weaker hypothesis, of rational expectation, is imposed. Thus, incumbents make mistakes about the median demand that are referred to as misperception errors. Thus, at a given point in time, incumbents can provide a bundle (given by the amount of expenditures per capita) that differs from median voter’s demand for public services by a multiplicative error term, which is included in the residuals of the demand equation. Next, it is estimated the impact of the module of this misperception error on the electoral performance of incumbents using a selection models. The result suggests that the median voter model is valid for the case of Brazilian municipalities.

Variabilidade espacial da textura de um latossolo vermelho eutroférrico sob cultivo de cana-de-açúcar

Estudos regionais mais detalhados, utilizando modelos de paisagem e geoestatística, têm demonstrado que, em áreas consideradas homogêneas, sob uma única classe de solo, existe dependência espacial de atributos granulométricos. Visando a avaliar a variabilidade espacial de atributos granulométricos em Latossolo Vermelho eutroférrico, foram feitas amostragens do solo em intervalos regulares de 50 m, em forma de malha, totalizando 306 pontos de amostragem. Foram coletadas amostras nas profundidades de 0-0,2 m e 0,6-0,8 m para a determinação da argila, silte, areia total (AT), areia grossa (AG), areia média (AM), areia fina (AF) e areia muito fina (AMF). Os dados foram submetidos à análise estatística descritiva, geoestatística e interpolação por krigagem. Os valores do coeficiente de variação apresentaram-se baixos para argila, médios para silte, AT, AF, AM e AMF e altos para AG. Observou-se ocorrência de dependência espacial para todas as variáveis com grau moderado de dependência espacial, com os maiores alcances ocorrendo na profundidade de 0-0,2 m. Os latossolos, apesar de serem homogêneos, mesmo em áreas de mesma classe de solo e manejo, apresentaram variabilidade diferenciada para os atributos granulométricos.

Estimação clássica e Bayesiana em modelos de sobrevida com fração de cura

In Survival Analysis, long duration models allow for the estimation of the healing fraction, which represents a portion of the population immune to the event of interest. Here we address classical and Bayesian estimation based on mixture models and promotion time models, using different distributions (exponential, Weibull and Pareto) to model failure time. The database used to illustrate the implementations is described in Kersey et al. (1987) and it consists of a group of leukemia patients who underwent a certain type of transplant. The specific implementations used were numeric optimization by BFGS as implemented in R (base::optim), Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can affect the Bayesian estimates

Variabilidade espacial do pH, Ca, Mg e V% do solo em diferentes formas do relevo sob cultivo de cana-de-açúcar

Em uma paisagem natural, os solos apresentam uma ampla variação dos atributos químicos, tanto vertical como horizontal, resultante da interação dos diversos fatores de formação envolvidos. Este trabalho foi desenvolvido em Guariba-SP, com o objetivo de avaliar a variabilidade espacial do pH, cálcio (Ca), magnésio (Mg) e saturação por bases (V%) em um Latossolo Vermelho eutroférrico sob cultivo de cana-de-açúcar, utilizando-se métodos da estatística clássica, análise geoestatística e técnica de interpolação de dados, com a finalidade de observar padrões de ocorrência destes atributos na paisagem. No terço inferior da encosta, após análise detalhada da variação do gradiente do declive, caracterizaram-se dois compartimentos (I e II), sob os quais os solos foram amostrados nos pontos de cruzamento de uma malha, com intervalos regulares de 50m, perfazendo um total de 206 pontos, nas profundidades de 0,0-0,2m e 0,6-0,8m. Os maiores alcances foram observados na profundidade de 0,0-0,2m para todos os atributos estudados, com exceção do cálcio que apresentou comportamento inverso, refletindo os efeitos do maior grau de intemperismo e do manejo na variabilidade natural dos solos. Pequenas variações, nas formas do relevo, condicionam variabilidade diferenciada para os atributos químicos.