Docente de Filosofia, Eros Moreira de Carvalho aponta que, em seu descaso em relação à verdade e à vida comum, o pós-negacionista não se preocupa em legitimar suas dúvidas sob a roupagem da racionalidade sensata

In this note we develop a new technique for parameter estimation of univariate time series by means of a parametric copula approach. The proposed methodology is based on a relationship between a process’ covariance decay and parametric bivariate copulas associated to lagged variables. This relationship provides a way for estimating parameters that are identifiable through the process’ covariance decay, such as in long range dependent processes. We provide a rigorous asymptotic theory for the proposed estimator. We also present a Monte Carlo simulation study to asses the finite sample performance of the proposed estimator.

In this work we introduce the class of Unit-Weibull Autoregressive Moving Average models for continuous random variables taking values in (0, 1). The proposed model is an observation driven one, for which, conditionally on a set of covariates and the process’ history, the random component is assumed to follow a Unit-Weibull distribution parameterized through its $$\rho $$th quantile. The systematic component prescribes an ARMA-like structure to model the conditional $$\rho $$th quantile by means of a link. Parameter estimation in the proposed model is performed using partial maximum likelihood, for which we provide closed formulas for the score vector and partial information matrix. We also discuss some inferential tools, such as the construction of confidence intervals, hypotheses testing, model selection, and forecasting. A Monte Carlo simulation study is conducted to assess the finite sample performance of the proposed partial maximum likelihood approach. Finally, we examine the prediction power by contrasting our method with others in the literature using the Manufacturing Capacity Utilization from the US.

Aplicações e dispositivos de inteligência artificial são cada vez mais comuns na área da saúde. Robôs que cumpram algumas funções de cuidado não são um futuro distante. Neste cenário, temos de nos perguntar se é possível haver máquinas capazes de cuidar a ponto de substituírem completamente o cuidado humano e se essa substituição, em sendo possível, é desejável. Neste artigo, argumento que o cuidado requer saberes-fazeres permeados por afetividade que estão longe de
serem realizados pelas máquinas atualmente disponíveis. Sustento também que a substituição completa do cuidado realizado por humanos por cuidado realizado por máquinas não é desejável porque o cuidado requer conexão humana real.

Copula models have become popular for minimum conditional value-at-risk (CVaR) portfolio optimization, especially due to their ability to deal with nonlinear dependencies. Nevertheless, as the number of assets in a portfolio increases, the estimation of copulas, particularly dynamic ones, becomes computationally burdensome. In this work, our novel contribution is to adapt and implement a dynamic factor copula model for the asset returns dependencies and find an optimal, potentially high dimensional, portfolio via minimizing its CVaR. The resulting model is capable of addressing the ``curse of dimensionality'' for the dependencies, while maintaining enough complexity and flexibility. The factor copula dynamics are described by a generalized autoregressive scores (GAS) model for the factor loadings. Using data consisting of B3 Brazilian stocks from January 2013 to December 2020, we find the optimal portfolio and evaluate its out of sample economic performance. Empirical results suggest that our min-CVaR-factor-copula strategy has either equal or better risk/return metrics when compared to a traditional Gaussian copula, while being considerably superior than both Markowitz mean-variance and equal weights portfolios as well as the IBRX50 index.

Breve artigo sobre a hipótese Gaia e a sua importância.