Modelos autorregressivos com valores inteiros não negativos : aplicação em séries económicas de Cabo Verde

No estudo de séries temporais, os processos estocásticos usuais assumem que as distribuições marginais são contínuas e, em geral, não são adequados para modelar séries de contagem, pois as suas características não lineares colocam alguns problemas estatísticos, principalmente na estimação dos parâmetros. Assim, investigou-se metodologias apropriadas de análise e modelação de séries com distribuições marginais discretas. Neste contexto, Al-Osh and Alzaid (1987) e McKenzie (1988) introduziram na literatura a classe dos modelos autorregressivos com valores inteiros não negativos, os processos INAR. Estes modelos têm sido frequentemente tratados em artigos científicos ao longo das últimas décadas, pois a sua importância nas aplicações em diversas áreas do conhecimento tem despertado um grande interesse no seu estudo. Neste trabalho, após uma breve revisão sobre séries temporais e os métodos clássicos para a sua análise, apresentamos os modelos autorregressivos de valores inteiros não negativos de primeira ordem INAR (1) e a sua extensão para uma ordem p, as suas propriedades e alguns métodos de estimação dos parâmetros nomeadamente, o método de Yule-Walker, o método de Mínimos Quadrados Condicionais (MQC), o método de Máxima Verosimilhança Condicional (MVC) e o método de Quase Máxima Verosimilhança (QMV). Apresentamos também um critério automático de seleção de ordem para modelos INAR, baseado no Critério de Informação de Akaike Corrigido, AICC, um dos critérios usados para determinar a ordem em modelos autorregressivos, AR. Finalmente, apresenta-se uma aplicação da metodologia dos modelos INAR em dados reais de contagem relativos aos setores dos transportes marítimos e atividades de seguros de Cabo Verde.

Predicción de volatilidad de la rentabilidad diaria del mercado del azúcar y su aplicación en la razón de cobertura

Este trabajo predice la volatilidad de la rentabilidad diaria del precio del azúcar, en el período compren­dido entre 1 de junio de 2011 y el 24 de octubre de 2013. Los datos diarios utilizados fueron los precios del azúcar, del etanol y la tasa de cambio de la moneda de Brasil (Real) en dólares. Se usaron modelos multivariados de volatilidad autoregresiva condicional generalizada. A partir de la predicción de los precios del azúcar se calcula la razón de cobertura de mínima varianza. Los resultados muestran, que la razón de cobertura es 0.37, esto significa que, si un productor adverso al riesgo, que tiene la intención de eliminar un porcentaje de la volatilidad de la rentabilidad diaria del mercado mundial del azúcar, y espera vender 25 contratos de azúcar, cada uno de ellos de 50,84 toneladas (1.271 toneladas), el número de contratos optimo tomando cobertura a futuro será 9 y el número de contratos sin tomar cobertura (de contado) será 16.

Modelos autorregressivos com valores inteiros não negativos : aplicação em séries económicas de Cabo Verde

No estudo de séries temporais, os processos estocásticos usuais assumem que as distribuições marginais são contínuas e, em geral, não são adequados para modelar séries de contagem, pois as suas características não lineares colocam alguns problemas estatísticos, principalmente na estimação dos parâmetros. Assim, investigou-se metodologias apropriadas de análise e modelação de séries com distribuições marginais discretas. Neste contexto, Al-Osh and Alzaid (1987) e McKenzie (1988) introduziram na literatura a classe dos modelos autorregressivos com valores inteiros não negativos, os processos INAR. Estes modelos têm sido frequentemente tratados em artigos científicos ao longo das últimas décadas, pois a sua importância nas aplicações em diversas áreas do conhecimento tem despertado um grande interesse no seu estudo. Neste trabalho, após uma breve revisão sobre séries temporais e os métodos clássicos para a sua análise, apresentamos os modelos autorregressivos de valores inteiros não negativos de primeira ordem INAR (1) e a sua extensão para uma ordem p, as suas propriedades e alguns métodos de estimação dos parâmetros nomeadamente, o método de Yule-Walker, o método de Mínimos Quadrados Condicionais (MQC), o método de Máxima Verosimilhança Condicional (MVC) e o método de Quase Máxima Verosimilhança (QMV). Apresentamos também um critério automático de seleção de ordem para modelos INAR, baseado no Critério de Informação de Akaike Corrigido, AICC, um dos critérios usados para determinar a ordem em modelos autorregressivos, AR. Finalmente, apresenta-se uma aplicação da metodologia dos modelos INAR em dados reais de contagem relativos aos setores dos transportes marítimos e atividades de seguros de Cabo Verde.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

A valuation of patients` willingness-to-pay for insulin delivery in diabetes

Objectives: The aim of this study was to determine the insulin-delivery system and the attributes of insulin therapy that best meet patients` preferences, and to estimate patients` willingness-to-pay (WTP) for them. Methods: This was a cross-sectional discrete choice experiment (DCE) study involving 378 Canadian patients with type 1 or type 2 diabetes. Patients were asked to choose between two hypothetical insulin treatment options made up of different combinations of the attribute levels. Regression coefficients derived using conditional logit models were used to calculate patients` WTP. Stratification of the sample was performed to evaluate WTP by predefined subgroups. Results: A total of 274 patients successfully completed the survey. Overall, patients were willing to pay the most for better blood glucose control followed by weight gain. Surprisingly, route of insulin administration was the least important attribute overall. Segmented models indicated that insulin naive diabetics were willing to pay significantly more for both oral and inhaled short-acting insulin compared with insulin users. Surprisingly, type 1 diabetics were willing to pay $C11.53 for subcutaneous short-acting insulin, while type 2 diabetics were willing to pay $C47.23 to avoid subcutaneous short-acting insulin (p < .05). These findings support the hypothesis of a psychological barrier to initiating insulin therapy, but once that this barrier has been overcome, they accommodate and accept injectable therapy as a treatment option. Conclusions: By understanding and addressing patients` preferences for insulin therapy, diabetes educators can use this information to find an optimal treatment approach for each individual patient, which may ultimately lead to improved control, through improved compliance, and better diabetes outcomes.

Indicadores avançados para o investimento directo estrangeiro: aplicação ao caso português

RESUMO:O investimento directo estrangeiro tem sido um dos factores com maior importância, no crescimento económico dos países em desenvolvimento, por contribuir para financiar o défice da balança corrente com o exterior, em particular a balança comercial. Num âmbito mais microeconómico é um forte gerador de emprego, proporciona avanços tecnológicos importantes, permitindo a partilha de conhecimentos das tecnologias, o conhecimento de novas formas de gestão e novas formas de marketing. Este trabalho tem como objectivo principal, identificar potenciais variáveis como indicadores avançados para o investimento directo estrangeiro, de modo a antecipar possíveis tendências para a sua evolução. Para alcançar este propósito recorreu-se aos Modelos Autoregressivos Vectoriais (VAR) e à causalidade de Granger com base em dados mensais para o período de Janeiro de 1996 a Setembro de 2010. Foram consideradas variáveis essenvialmente macroeconómicas, tanto do lado da economia receptora como dos países investidores, de modo a reflectirem a actividade económica ao longo do período de estudo. ABSTRACT: The foreign direct investment, has been one of the main factors in the economical development for the countries that are in a process of developing, because it allows the generation of new investments and generate money from the return of the investment, as well as it creates new opportunities for the employment. It allows important technologic advances with the share of the technology Knowledge as well new ways to learn marketing management and enterprise management. This work/research, aims to identify potential variables as advanced indicators for the foreign direct investment, in order to anticipate possible trends of their evolution. To achieve this goal, Vector Autoregressive Models (VAR) and Granger causality based on based on monthly data for the period January between 1996 and September of 2010, were used. Essentially macroeconomic variables were considered, on both the host economy and the countries investors in order to reflect the economic activity throughout the study period.

Estudo sobre a memória longa na volatilidade dos preços do ouro : uma abordagem com base nos modelos de tipo ARCH

Mestrado em Contabilidade e Análise Financeira

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Modeling and forecasting value-at-risk for the Portuguese stock market

The aim of this work project is to find a model that is able to accurately forecast the daily Value-at-Risk for PSI-20 Index, independently of the market conditions, in order to expand empirical literature for the Portuguese stock market. Hence, two subsamples, representing more and less volatile periods, were modeled through unconditional and conditional volatility models (because it is what drives returns). All models were evaluated through Kupiec’s and Christoffersen’s tests, by comparing forecasts with actual results. Using an out-of-sample of 204 observations, it was found that a GARCH(1,1) is an accurate model for our purposes.

Real convergence in per capita output and growth in Eurozone: A time-series approach

The real convergence hypothesis has spurred a myriad of empirical tests and approaches in the economic literature. This Work Project intends to test for real output and growth convergence in all N(N-1)/2 possible pairs of output and output growth gaps of 14 Eurozone countries. This paper follows a time-series approach, as it tests for the presence of unit roots and persistence changes in the above mentioned pairs of output gaps, as well as for the existence of growth convergence with autoregressive models. Overall, significantly greater evidence has been found to support growth convergence rather than output convergence in our sample.

Stock market integration between new EU member states and the Euro-zone

This paper measures the degree in stock market integration between five Eastern European countries and the Euro-zone. A potentially gradual transition in correlations is accommodated by smooth transition conditional correlation models. We find that the correlation between stock markets has increased from 2001 to 2007. In particular, the Czech and Polish markets show a higher correlation to the Euro-zone. However, this is not a broad-based phenomenon across Eastern Europe. We also find that the increase in correlations is not a reflection of a world-wide phenomenon of financial integration but appears to be specific to the European market. JEL classifications: C32; C51; F36; G15 Keywords: Multivariate GARCH; Smooth Transition Conditional Correlation; Stock Return Comovement; New EU Members.

Progress Towards to Equity Market Integration in Eastern Europe

The advent of the European Union has decreased the diversification benefits available from country based equity market indices in the region. This paper measures the increase in stock integration between the three largest new EU members (Hungary, the Czech Republic and Poland who joined in May 2004) and the Euro-zone. A potentially gradual transition in correlations is accommodated in a single VAR model by embedding smooth transition conditional correlation models with fat tails, spillovers, volatility clustering, and asymmetric volatility effects. At the country market index level all three Eastern European markets show a considerable increase in correlations in 2006. At the industry level the dates and transition periods for the correlations differ, and the correlations are lower although also increasing. The results show that sectoral indices in Eastern European markets may provide larger diversification opportunities than the aggregate market. JEL classifications: C32; C51; F36; G15 Keywords: Multivariate GARCH; Smooth Transition Conditional Correlation; Stock Return Comovement; Sectoral correlations; New EU Members

3-Regime symmetric STAR modeling and exchange rate reversion

The breakdown of the Bretton Woods system and the adoption of generalized oating exchange rates ushered in a new era of exchange rate volatility and uncer- tainty. This increased volatility lead economists to search for economic models able to describe observed exchange rate behavior. In the present paper we propose more general STAR transition functions which encompass both threshold nonlinearity and asymmetric e¤ects. Our framework allows for a gradual adjustment from one regime to another, and considers threshold e¤ects by encompassing other existing models, such as TAR models. We apply our methodology to three di¤erent exchange rate data-sets, one for developing countries, and o¢ cial nominal exchange rates, the sec- ond emerging market economies using black market exchange rates and the third for OECD economies.

Technical Appendix-3-Regime asymmetric STAR modeling and exchange rate reversion

The breakdown of the Bretton Woods system and the adoption of generalized oating exchange rates ushered in a new era of exchange rate volatility and uncer- tainty. This increased volatility lead economists to search for economic models able to describe observed exchange rate behavior. The present is a technical Appendix to Cerrato et al. (2009) and presents detailed simulations of the proposed methodology and additional empirical results.

Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.