According to a study commissioned by the National Institute of Standards and Technology in 2002, software bugs cost the U.S. economy an estimated $59.5 billion annually, or about 0.6 percent of the nation’s gross domestic product (GDP). The same study also found that more than one-third of these costs, or an estimated $22.2 billion, could be eliminated by an improved testing infrastructure. The...

Scattered works by political scientists since the 1970s have reported that Democratic presidents have compiled stronger economic records than their Republican counterparts: economic growth has been higher, unemployment lower, and inequality has fallen during Democratic administrations while the opposite outcomes have occurred under Republican presidents. Recently, however, Campbell has vigorous...

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final...

Empirical studies quantifying the benefits of increased foreign direct investment (FDI) have been unable to provide conclusive evidence of a positive impact on host country’s economic performance. I show that the lack of robust evidence is not inconsistent with theory, even if the eventual gains to FDI are large, if restrictions on FDI are lifted only gradually and part of FDI is intangible inv...

The impact and significance of infrastructure development towards the economic growth of a country cannot be overemphasised. This is because it is a major component that is required to ensure an increase in domestic productivity and attract foreign direct investment (FDI) inflow. This study through the use of Ordinary Least Squares and Granger Causality econometric techniques investiages the in...

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final...

We analyse, from a worldwide view, the evolution of real Gross Domestic Product, Population and Gdp per inhabitant and the main differences in the levels of development are explained having into account the results of several cross-country models. The main conclusions highlight the necessity of new international policies for improving the educative level of population in less developed countrie...

Agriculture is one of the easiest sectors of the U.S. economy to disrupt, and its disruption could have catastrophic consequences for the U.S. and world economies. Agriculture in the U.S. accounts for 13% of the current Gross Domestic Product (GDP) and provides employment for 15% of the population. It produces high-quality, cheap, plentiful food for domestic consumption and accounts for more th...