Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.

Um modelo de competição baseada em tempo nos mercados financeiros

O objetivo deste trabalho é modelar o comportamento estratégico dos indivíduos diante de um choque estocástico que desloca o preço de determinado ativo financeiro do seu equilíbrio inicial. Investiga-se o caminho do preço de mercado em direção ao novo equilíbrio, conduzido pelas sucessivas negociações dos agentes em busca de oportunidades de obter lucros imediatos. Os operadores, que por suposição possuem funções de utilidade avessas ao risco, devem escolher a quantidade ótima transacionada e quanto devem aguardar para executar as suas ordens, tendo em vista a diminuição da volatilidade do preço do ativo à medida que as transações se sucedem após o choque. Procura-se demonstrar que os operadores que aceitam incorrer em riscos mais elevados negociam com maior frequência e em volumes e velocidades maiores, usufruindo lucros esperados mais altos que os demais.

Intrahousehold allocation in an unintentional random experiment

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Estimating the stochastic discount factor without a utility function

Using the Pricing Equation in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) which relies on the fact that its logarithm is the serial-correlation ìcommon featureîin every asset return of the economy. Our estimator is a simple function of asset returns, does not depend on any parametric function representing preferences, is suitable for testing di§erent preference speciÖcations or investigating intertemporal substitution puzzles, and can be a basis to construct an estimator of the risk-free rate. For post-war data, our estimator is close to unity most of the time, yielding an average annual real discount rate of 2.46%. In formal testing, we cannot reject standard preference speciÖcations used in the literature and estimates of the relative risk-aversion coe¢ cient are between 1 and 2, and statistically equal to unity. Using our SDF estimator, we found little signs of the equity-premium puzzle for the U.S.

A teoria da eficiência no mercado de capitais: uma revisão da literatura e dos trabalhos empíricos: o modelos de Random walk aplicado ao índice de mercado de ações Bovespa

Durante muitos anos uma controversa questão tem ocupado tanto os discursos acadêmicos quanto os financeiros. O problema a ser resolvido diz respeito à evolução passada dos preços das ações e se tal evolução poderia ser utilizada para prever o comportamento dos preços futuros dessas ações.