Taylor principle and inflation stability in emerging market countriesw

The goal of this paper is to evaluate the validity of the Taylor principle for inflation control in 12 developing countries that use inflation targeting regimes: Brazil, Chile, Colombia, Hungary, Israel, Mexico, Peru, Philippines, Poland, South Africa, Thailand and Turkey. The test is based on a state-space model to determine when each country has followed the principle; then a threshold unit root test is used to verify if the stationarity of the deviation of the expected inflation from its target depends on compliance with the Taylor principle. The results show that such compliance leads to the stationarity of the deviation of the expected inflation from its target in all cases. Furthermore, in most cases, non-compliance with the Taylor principle leads to nonstationary deviation of the expected inflation.

Proposta de modelo teórico de treinamento e desenvolvimento de colaboradores nas empresas de transformação industrial: cadeias do cliente, fornecedor, valor, extended supply chain, innovation chain, integrated supply chain e supply chain

Trata de propor um modelo teórico de treinamento & desenvolvimento para a efetiva disseminação dos conceitos pertinentes ao integrated supply chain centrado no homem, observando as vertentes científicometodológica, digital, econõnu ca, psicológica, social e técnica e embasado na teoria do impacto, que compreende o ambiente de trabalho, experiência do aprendizado e características individuais. Com base nesse modelo, o autor advoga a necessidade de transformar os modelos de pesquisa, desenvolvimento e difusão, interação social e tomada de decisão e resolução de problemas ~ropostos por Havelock na década de 60, respectivamente, em Disseminação, Facilitação e Inovação.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.