[pt] Modelos estruturais para séries temporais vêm sendo bastante utilizados ultimamente e, adotam, basicamente, a mesma idéia da decomposição clássica de uma série temporal em seus componentes não-observáveis: tendência, sazonalidade, cíclica e irregular; para a componente cíclica, em particular, que é modelada por uma senóide a amortecida, existem apenas soluções no contexto da Estatística Clássica Harvey (1985). Neste trabalho discutimos extensivamente a solução Bayesiana para o modelo, tornando completamente estocástico a componente ciclo e obtendo um algoritmo para a estimação seqüencial dos parâmetros. A natureza não linear do problema é tratada pelos Modelos Dinâmicos Bayesianos; West e Harrison (1986). / [en] The structural models for time series, so much in use today make use of the well know idea of decomposing a time series into its unobserved components of trend, seasonal, cycle and noise. The cyclical component in particular, which uses a damped sine wave to describe its moviment, has a clear solution available already in computer packages on the Classica framework of Harvey (1985). In this thesis we present a Bayesian solution to the cyclical component modelled by the same damped sine wave. The frequency and the damping factor, regarded as hyperparameters on the Classical solution are now incorporated to the system state vector and estimated by a sequential procedure. Finally, the non-linear nature of model is elegantly dealt with by the Bayesian Dynamic Models of West and Harrison (1986).

A energia elétrica tem papel fundamental em todos os lugares do mundo e, no Brasil, a importância não poderia ser menor. Com sua implantação no país no final do século XIX, o setor passou por diversos períodos de crescimento com características distintas. A economia nacional, de forma similar ao setor elétrico, ao longo do mesmo período passou por fases de grande expansão e por fases de crise. Nesse contexto, o presente trabalho analisa a evolução da economia e do setor elétrico nacionais desde o final do século XIX até o ano de 2010 e, com isso, busca investigar de que forma se relacionaram os crescimentos de um e de outro. Para isso são conduzidas análises histórica e econométrica. A primeira é feita por meio de revisão da bibliografia pertinente ao tema em que o período do estudo é dividido em sete partes: a primeira aborda o início da indústria elétrica brasileira e vai até o final da Primeira República (1889-1930), a segunda abrange o início do governo Getúlio Vargas até o início da ditadura militar (1930-1964), a terceira trata do período da ditadura (1964-1985), a quarta engloba a fase do início da República Nova até a estruturação do setor elétrico (1985-1994), a quinta analisa o período entre a reestruturação do setor elétrico e a crise energética (1995-2002), a sexta trata da contra-reforma do setor elétrico e também de suas características de mercados (2002-2003) e, por fim a última parte analisa o setor elétrico e economia contemporâneos (2003-2010). Na análise econométrica utilizada nesta dissertação testa-se, de forma quantitativa, as evidências de existência de relação de longo prazo entre a economia e o setor elétrico através de um teste de cointegração. A seguir, é conduzida uma análise econométrica adicional com a qual, por meio de um modelo estrutural de séries de tempo, estima-se a demanda de energia elétrica para os próximos anos no Brasil. Com essa ferramenta de projeção de demanda pretende-se oferecer subsídios para o planejamento da expansão da capacidade instalada de geração de energia elétrica do sistema brasileiro. Por fim, analisa-se a validade dos resultados econométricos à luz do contexto histórico desenvolvido anteriormente e apresentam-se conclusões e limitações para este trabalho. / Electricity plays a fundamental role in all parts of the world and in Brazil the importance could not be lower. With its beginnings in the country in the late nineteenth century, the power sector has gone through several periods of growth with distinct characteristics. The national economy, similarly to the electricity sector, over the same period went through phases of boom and phases of crisis. In this context, this dissertation analyzes the evolution of the economy and of the national power sector since its beginning until the year 2010 and, therefore, seeks to investigate how the increases of one were related to the other. Historical and econometric analyses are conducted for this purpose. The first is through the review of the literature relevant to the topic, where the period of study is divided into seven parts: the first is from the start of the Brazilian electrical industry and runs until the end of the First Republic (1889-1930), the second is from beginning of the Getulio Vargas government until the beginning of the military dictatorship (1930-1964), the third deals with the period of the dictatorship (1964-1985), the fourth phase studies from the beginning of the New Republic to the structuring of the electricity sector (1985-1994), the fifth examines the period between the electricity sector restructuring and the energy crisis (1995-2002), the sixth comes from the electricity sector reform as well as its market characteristics (2002-2003) and finally the last part examines the contemporary electricity sector and economy (2003-2010). In econometric analysis tests used in this dissertation, it is tested, quantitatively, the evidence of the existence of long-term relationship between the economy and energy sector through a cointegration test. Next, an additional econometric analysis is conducted where, through a structural time series model, it is estimated the demand for electricity for the next years in Brazil. With this demand projection tool, it is intended to provide background information for planning the expansion of installed capacity of electric power generation system in Brazil. Finally the validity of the econometric results is analyzed in the light of the historical context previously developed, and conclusions and limitations of this paper are presented.

Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.

Objective: The project aimed to compare marginal structural models, and propensity score adjusted models with Cox Proportional Hazards models to address confounding by indication due to time-dependent confounders. These methods were applied to data from approximately 120,000 women in the Women’s Health Initiative to evaluate the causal effect of antidepressant medication with respect to diabetes risk. Methods: Four approaches were compared. Three Cox Models were used. The first used baseline covariates. The second used time-varying antidepressant medication use, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates. The third used time-varying antidepressant medication use, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates and propensity to taking antidepressants at baseline. Our fourth method used a Marginal Structural Cox Model with Inverse Probability of Treatment Weighting that included time-varying antidepressant medication, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates. Results: All approaches showed an increase in diabetes risk for those taking antidepressants. Diabetes risk increased with adjustment for time-dependent confounding and results for these three approaches were similar. All models were statistically significant. Ninety-five percent confidence intervals overlapped for all approaches showing they were not different from one another. Conclusions: Our analyses did not find a difference between Cox Proportional Hazards Models and Marginal Structural Cox Models in the WHI cohorts. Estimates of the Inverse Probability of Treatment Weights were very close to 1 which explains why we observed similar results.

Depression increases the risk of suicide death and non-fatal suicide attempt. Between 2 - 6% of persons with depression will die by suicide1 and 25 - 31% of persons with depression will make a non-fatal suicide attempt during their lifetime.2,3 Despite the strong association between depression and suicidal behavior, the vast majority of persons with depression will not engage in suicidal behavior, making it difficult to accurately predict who is at risk for suicide and non-fatal suicide attempt. Identifying high risk persons who should be connected to suicide prevention interventions is an important public health goal. Furthermore, depression often co-occurs with other mental disorders, which may exert an interactive influence on the risk of suicide and suicide attempt. Understanding the joint influence of depression and other mental disorders on suicide outcomes may inform prevention strategies. The goals of this dissertation were to predict suicide and non-fatal suicide attempt among persons with depression and to quantify the causal joint effect of depression and comorbid psychiatric disorders on suicide and suicide attempt. For all three studies, we used data from Danish registries, which routinely collect high-quality data in a setting of universal health care with long-term follow-up and registration of most health and life events.4 In Study 1, we predicted suicide deaths among men and women diagnosed with depression using a case-cohort design (n = 14,737). Approximately 800 predictors were included in the machine learning models (classification trees and random forests), spanning demographic characteristics, income, employment, immigrant status, citizenship, family suicidal history (parent or spouse), previous suicide attempts, mental disorders, physical health disorders, surgeries, prescription drugs, and psychotherapy. In depressed men, we found interactions between hypnotics and sedatives, analgesics and antipyretics, and previous poisonings that were associated with a high risk of suicide. In depressed women, there were interactions between poisoning and anxiolytics and between anxiolytics and hypnotics and sedatives that were associated with suicide risk. The variables in the random forests that contributed the most to prediction accuracy in depressed men were previous poisoning diagnoses and prescriptions of hypnotics and sedatives and anxiolytics. In depressed women, the most important predictors of suicide were receipt of state pension, prescriptions for psychiatric medications (anxiolytics and antipsychotics) and diagnoses of poisoning, alcohol related disorders, and reaction to severe stress and adjustment disorders. Prescriptions of analgesics and antipyretics (e.g., acetaminophen) and antithrombotic agents (e.g., aspirin) emerged as important predictors for both depressed men and women. Study 2 predicted non-fatal suicide attempts among men and women diagnosed with depression using a case-cohort design (n = 17,995). Among depressed men, there was a high risk of suicide attempt among those who received a state pension and were diagnosed with toxic effects of substances. There was also an interaction between reaction to severe stress and adjustment disorder and not receiving psychological help that was associated with suicide attempt risk among depressed men. In depressed women, suicide attempt risk was high in those who were prescribed antipsychotics, diagnosed with specific personality disorders, did not have a poisoning diagnosis, and were not receiving a state pension. For both men and women, the random forest results showed that the strongest contributors to prediction accuracy of suicide attempts were poisonings, alcohol related disorders, reaction to severe stress and adjustment disorders, drugs used to treat psychiatric disorders (e.g., drugs used in addictive disorders, anxiolytics, hypnotics and sedatives), anti-inflammatory medications, receipt of state pension, and remaining single. Study 3 examined the joint effect of depression and other mental disorders on suicide and non-fatal suicide attempts using a case-cohort design (suicide death analysis n = 279,286; suicide attempt analysis n = 288,157). We examined pairwise combinations of depression with: 1) organic disorders, 2) substance use disorders, 3) schizophrenia, 4) bipolar disorder, 5) neurotic disorders, 6) eating disorders, 7) personality disorders, 8) intellectual disabilities, 9) developmental disorders, and 10) behavioral disorders. We fit sex-stratified joint marginal structural Cox models to account for time-varying confounding. We observed large hazard ratios for the joint effect of depression and comorbid mental disorders on suicide and suicide attempts, the effect of depression in the absence of comorbid mental disorders, and for the effect of comorbid mental disorders in the absence of depression. We observed positive and negative interdependence between different combinations of depression and comorbid mental disorders on the rate of suicide and suicide attempt, with variation by sex. Overall, depression and comorbid mental disorders are harmful exposures, both independently and jointly. All of the studies in this dissertation highlight the important role of interactions between risk factors in suicidal behavior among persons with depression. Depression is one of the most commonly assessed risk factors for suicide,5,6 and our findings underscore the value of considering additional risk factors such as other psychiatric disorders, psychiatric medications, and social factors in combination with depression. The results of this dissertation may help inform potential risk identification strategies which may facilitate the targeting of suicide prevention interventions to those most vulnerable.

This dissertation consists of three essays on the empirics of firms' strategies in new industries. In the first chapter, I study the spatial mismatch between consumers and bikes in the dockless bike-sharing industry and an externality exacerbating the problem: when a consumer uses a bike for a low and inflexible price, she both displaces another consumer's usage for a potential higher-value trip, and may ride the bike to unpopular destinations. With a trip-level dataset of a bike-sharing company in Beijing, China, I develop a spatial structural model to estimate the demand for bikes with search frictions and local matchings. Compared to the scenario in which consumers always get bikes immediately, I find that local spatial mismatch between consumers and bikes reduces the total usage by 29.95%, or a net loss of 332,979 trips. Counterfactual analyses show that (1) doubling the number of bikes increases the trip volume by 28.46% while halving the number of bikes decreases the trip volume by 46.40%; (2) price-discriminating against short trips by 2% increases the total trip time by 0.22%; and (3) changing the frequency of bike reshuffling does not have a significant impact on the total usage of bikes. In the second chapter, I study how efficient capital markets are in supplying funds to new firms by looking at how a platform start-up, ofo, made its investment decisions in response to capital infusions. I fit the business performance of ofo, a bike-sharing platform start-up, in China and show how its financial conditions affected investment decisions. I analyze the effects of funding rounds from venture capitalists on the investment and business of the company with an event study framework. My estimates find that the firm increased its users and bikes by about 40% two weeks before receiving funds, suggesting that it spent much more on bike fleet and promotional offers in expectation of capital infusions. I also show that such boosts in business performance were short-lived: the number of trips and users often returned to normal levels two weeks after the funding day. My findings suggest that the capital market is inefficient in supplying funds to start-up companies. In the third chapter, I study the shakeout in the U.S. automobile industry with data retrieved from old annals of the automobile industry. I simulate a research productivity model and see if I could successfully trigger a shakeout. I find that only the cost reduction from technology advancements is not enough to trigger an industry shakeout and propose that more extreme settings are needed for further studies.

No description available.

Credit spread is the extra risk-reward that an investor is bearing for investing in corporate bonds instead of government bonds. Structural models, which are simple in their framework, fail to explain the occurring credit spread and underestimate the predicted credit spread. Hence, the need for new models and exploration of systematic and idiosyncratic variables arose. The present paper aims to investigate if the predictability of lower-medium investment grade bonds and non-investment grade bonds credit spread can be improved by incorporating systematic and idiosyncratic variables into a fixed effect panel data regression model, and whether the selected variables’ significance has high influence on credit spread or not. Initial results showed that fixed effect panel data regression model underperforms the structural models and under predicts the actual credit spread. The applied model explained 13.5% of the lower-medium investment grade bonds credit spread and 8.5% of non-investment grade bonds. Further, systematic variables have higher influence on lower-medium investment grade bonds and idiosyncratic variables have higher influence on non-investment grade bonds. The predictability of credit spread can be improved by employing correct explanatory variables which are selected based on the characteristics of the sample size.

The aim of this thesis is to investigate possible improvement of scoring models prediction power in retail credit segment by using structural models estimating the future development of behavioral score. These models contain the informa- tion about past development of the behavioral score by parameters which take into account the sensitivity of clients' probability of default on individual market and life changes. These parameters are estimated by Markov Chain Monte Carlo methods based on score history. Eight different types of structural models were applied to real data. The diversification measure of individual models is compared using the Gini coefficient. These structural models were compared with each other and also with the existing scoring model of the credit institution which provided the underlying data. 1

O objetivo da análise estrutural é determinar os efeitos das ações em uma estrutura, com a finalidade de efetuar verificações de estados limites últimos e de serviço (NBR 6118:2003 Projeto de estruturas de concreto). A análise estrutural consiste numa das principais etapas do projeto estrutural de um edifício, pois compreende a escolha dos modelos teóricos, que devem representar adequadamente a estrutura real, e do tipo de análise, com relação ao comportamento dos materiais. Visa-se, com este trabalho, aproximar o meio técnico do acadêmico, e tornar mais acessível a parte da NBR 6118:2003 que trata da análise estrutural. Neste trabalho consideram-se os modelos estruturais mais utilizados, no cálculo de edifícios, e os principais conceitos relativos aos diferentes tipos de análise permitidos pela norma. Em seguida são realizados exemplos de elementos lineares, pelos diferentes tipos de análise, com ênfase na redistribuição de esforços, empregando-se análise linear com redistribuição ou análise plástica. Ressalta-se a importância da consideração de seção T nas vigas e os ajustes necessários quando da consideração de uma envoltória de carregamentos. Tem-se ainda um exemplo de um edifício de oito pavimentos, que visa demonstrar as diferenças nos esforços ou nos deslocamentos obtidos com modelos estruturais distintos / The structural analysis objective is to determine the actions effects in a structure, with the purpose of verifying the ultimate limit states and serviceability (brazilian code NBR 6118:2003 - Design of concrete structures). The structural analysis is one of the main parts of a building structural design, since it involves the choice of theoretical models that represent appropriately the real structure, and the type of analysis due to the materials behavior. This work intends to shorten the distance between design engineers and the academic world, providing a clearer vision of the NBR 6118:2003 structural analysis approach. This work considers the most common structural models used for buildings, and the theory concerning the different types of analysis permitted by the brazilian code. It presents simple examples of linear elements computed by different types of analysis, emphasizing the moment redistribution possibility, either with the linear analysis with redistribution or the plastic analysis. The importance of considering T-beam with moment redistribution is made clear, and lines of direction to consider alternate position of live loads are given. The last example presents an eight store building, and its differences relative to efforts and displacements, obtained by distinct structural models