Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Analysis Of Semi-Distributed And Global Hydrological Models In The Central Tropical Basins Of The Gulf Of Mexico To The Effects Of Extreme Hydrometeorological Phenomena

In the last years extreme hydrometeorological phenomena have increased in number and intensity affecting the inhabitants of various regions, an example of these effects are the central basins of the Gulf of Mexico (CBGM) that they have been affected by 55.2% with floods and especially the state of Veracruz (1999-2013), leaving economic, social and environmental losses. Mexico currently lacks sufficient hydrological studies for the measurement of volumes in rivers, since is convenient to create a hydrological model (HM) suited to the quality and quantity of the geographic and climatic information that is reliable and affordable. Therefore this research compares the semi-distributed hydrological model (SHM) and the global hydrological model (GHM), with respect to the volumes of runoff and achieve to predict flood areas, furthermore, were analyzed extreme hydrometeorological phenomena in the CBGM, by modeling the Hydrologic Modeling System (HEC-HMS) which is a SHM and the Modèle Hydrologique Simplifié à I'Extrême (MOHYSE) which is a GHM, to evaluate the results and compare which model is suitable for tropical conditions to propose public policies for integrated basins management and flood prevention. Thus it was determined the temporal and spatial framework of the analyzed basins according to hurricanes and floods. It were developed the SHM and GHM models, which were calibrated, validated and compared the results to identify the sensitivity to the real model. It was concluded that both models conform to tropical conditions of the CBGM, having MOHYSE further approximation to the real model. Worth mentioning that in Mexico there is not enough information, besides there are no records of MOHYSE use in Mexico, so it can be a useful tool for determining runoff volumes. Finally, with the SHM and the GHM were generated climate change scenarios to develop risk studies creating a risk map for urban planning, agro-hydrological and territorial organization.

Redefining the Academic Library Managing the Migration to Digital Information Services

Academic libraries are faced with a daunting series of challenges brought on by the digital revolution. In an era when millions of books, articles, images, and videos available instantaneously via the web, libraries across all institutional types are experiencing declining demand for their traditional services, built around the storage and dissemination of physical resources. At the same time, new demand for digital information services and collaborative learning spaces promise new areas of opportunity and engagement with patrons. A rapid and orderly transition to “the library of the future” requires difficult trade-offs, however, as no institution can afford to continue expanding both its commitment to comprehensive, local print collections as well as new investments in staff, technology, and renovations. This report illustrates how progressive academic libraries are evolving in response to these challenges, providing case studies and best practices in managing library space, staff, and resources.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

Essays on asymmetric information problems without the single-crossing property: applications to corporate finance

The objective of this dissertation is to re-examine classical issues in corporate finance, applying a new analytical tool. The single-crossing property, also called Spence-irrlees condition, is not required in the models developed here. This property has been a standard assumption in adverse selection and signaling models developed so far. The classical papers by Guesnerie and Laffont (1984) and Riley (1979) assume it. In the simplest case, for a consumer with a privately known taste, the single-crossing property states that the marginal utility of a good is monotone with respect to the taste. This assumption has an important consequence to the result of the model: the relationship between the private parameter and the quantity of the good assigned to the agent is monotone. While single crossing is a reasonable property for the utility of an ordinary consumer, this property is frequently absent in the objective function of the agents for more elaborate models. The lack of a characterization for the non-single crossing context has hindered the exploration of models that generate objective functions without this property. The first work that characterizes the optimal contract without the single-crossing property is Araújo and Moreira (2001a) and, for the competitive case, Araújo and Moreira (2001b). The main implication is that a partial separation of types may be observed. Two sets of disconnected types of agents may choose the same contract, in adverse selection problems, or signal with the same levei of signal, in signaling models.

Precificação de opções de dólar no mercado brasileiro utilizando redes neurais e algoritmos genéticos

Esse trabalho comparou, para condições macroeconômicas usuais, a eficiência do modelo de Redes Neurais Artificiais (RNAs) otimizadas por Algoritmos Genéticos (AGs) na precificação de opções de Dólar à Vista aos seguintes modelos de precificação convencionais: Black-Scholes, Garman-Kohlhagen, Árvores Trinomiais e Simulações de Monte Carlo. As informações utilizadas nesta análise, compreendidas entre janeiro de 1999 e novembro de 2006, foram disponibilizadas pela Bolsa de Mercadorias e Futuros (BM&F) e pelo Federal Reserve americano. As comparações e avaliações foram realizadas com o software MATLAB, versão 7.0, e suas respectivas caixas de ferramentas que ofereceram o ambiente e as ferramentas necessárias à implementação e customização dos modelos mencionados acima. As análises do custo do delta-hedging para cada modelo indicaram que, apesar de mais complexa, a utilização dos Algoritmos Genéticos exclusivamente para otimização direta (binária) dos pesos sinápticos das Redes Neurais não produziu resultados significativamente superiores aos modelos convencionais.

Evaluating Value-at-Risk models via Quantile regressions

This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables to do this sacrifices too much information. However, most of the specification tests (also called backtests) avaliable in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not realy solely on binary variable. It is show that the new backtest provides a sufficiant condition to assess the performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theorical findings are corroborated through a monte Carlo simulation and an empirical exercise with daily S&P500 time series.

The macroeconomic effects of infrequent information with adjustment costs

We extend the macroeconomic literature on Sstype rules by introducing infrequent information in a kinked ad justment cost model. We first show that optimal individual decision rules are both state-and -time dependent. We then develop an aggregation framework to study the macroeconomic implications of such optimal individual decision rules. In our model, a vast number of agents act together, and more so when uncertainty is large.The average effect of an aggregate shock is inversely related to its size and to aggregate uncertainty. These results are in contrast with those obtained with full information ad justment cost models.

The macroeconomics of price adjustments under information frictions and menu costs

Esta tese se dedica ao estudo de modelos de fixação de preços e suas implicações macroeconômicas. Nos primeiros dois capítulos analiso modelos em que as decisões das firmas sobre seus preços praticados levam em conta custos de menu e de informação. No Capítulo 1 eu estimo tais modelos empregando estatísticas de variações de preços dos Estados Unidos, e concluo que: os custos de informação são significativamente maiores que os custos de menu; os dados claramente favorecem o modelo em que informações sobre condições agregadas são custosas enquanto que as idiossincráticas têm custo zero. No Capítulo 2 investigo as consequências de choques monetários e anúncios de desinflação usando os modelos previamente estimados. Mostro que o grau de não-neutralidade monetária é maior no modelo em que parte da informação é grátis. O Capítulo 3 é um artigo em conjunto com Carlos Carvalho (PUC-Rio) e Antonella Tutino (Federal Reserve Bank of Dallas). No artigo examinamos um modelo de fixação de preços em que firmas estão sujeitas a uma restrição de fluxo de informação do tipo Shannon. Calibramos o modelo e estudamos funções impulso-resposta a choques idiossincráticos e agregados. Mostramos que as firmas vão preferir processar informações agregadas e idiossincráticas conjuntamente ao invés de investigá-las separadamente. Este tipo de processamento gera ajustes de preços mais frequentes, diminuindo a persistência de efeitos reais causados por choques monetários.

The promised LAN: the transformative power of information and communications technology in developing countries

Este tese analise as implicações dos investimentos em tecnologia de informação e comunicação (ICT) em países ainda em desenvolvimento, especialmente em termos de educação, para estimular a implementação de uma infra-estrutura mais moderna em vez da continuação do uso de métodos tradicionais. Hoje, como o interesse e os investimentos em ICT estão crescendo rapidamente, os módulos e as idéias que existem para medir o estado de ICT são velhos e inexatos, e não podem ser aplicados às culturas de países em desenvolvimento. Políticos e investidores têm que considerar estes problemas quando estão pensando em investimentos ou socorros para programas em ICT no futuro, e investigadores e professores precisam entender os fatores importantes no desenvolvimento para os ICTs e a educação antes de começar estudos nestes países. Este tese conclue que investimentos em tecnologias móbeis e sem fios ajudarem organizações e governos ultrapassar a infra-estrutura tradicional, estreitando a divisão digital e dando o resulto de educação melhor, alfabetização maior, e soluções sustentáveis pelo desenvolvimento nas comunidades pobres no mundo de países em desenvolvimento.

Hedging options in a garch environment: testing the term structure of stochastic volatility models

This paper develops a methodology for testing the term structure of volatility forecasts derived from stochastic volatility models, and implements it to analyze models of S&P500 index volatility. U sing measurements of the ability of volatility models to hedge and value term structure dependent option positions, we fmd that hedging tests support the Black-Scholes delta and gamma hedges, but not the simple vega hedge when there is no model of the term structure of volatility. With various models, it is difficult to improve on a simple gamma hedge assuming constant volatility. Ofthe volatility models, the GARCH components estimate of term structure is preferred. Valuation tests indicate that all the models contain term structure information not incorporated in market prices.