%Gra: an SAS macro for generalized redundancy analysis

In the framework of redundancy analysis and reduced rank regression, the extended redundancy analysis model managed to account for more than two blocks of manifest variables in its specification. A further extension, the generalized redundancy analysis (GRA), has been recently proposed in literature, with the aim of incorporating external covariates into the model, thanks to a new estimation algorithm that manages to separate all the contributions of the exogenous and external covariates in the formation of the latent composites. At present, software to estimate GRA models is not available. In this paper, we provide an SAS macro, %GRA, to specify and fit structural relationships, with an application to illustrate the use of the macro.     

Technological change and technology monitoring process: Evidence from four Italian case studies

Technology monitoring (TM) may take on different meanings in a company’s day-to-day activities. In some cases it can be interpreted as a technology intelligence methodology, while in others it is understood as technology forecasting or even as technology assessment. These different interpretations are confirmed by an analysis of the literature. This case study examines the technology monitoring process in four Italian companies.For each of the cases considered, first we analysed the way technology monitoring is carried out and then we tried to understand what factors influence the company approach to technology monitoring. Four factors that can be expected to influence a company’s technology monitoring approach came out of this empirical research study: the industry a company operates in, the business model it uses, the importance of technology in the corporate culture, and the level of its R&D resources.     

On the max-weight edge coloring problem

We study the following generalization of the classical edge coloring problem: Given a weighted graph, find a partition of its edges into matchings (colors), each one of weight equal to the maximum weight of its edges, so that the total weight of the partition is minimized. We explore the frontier between polynomial and NP-hard variants of the problem, with respect to the class of the underlying graph, as well as the approximability of NP-hard variants. In particular, we present polynomial algorithms for bounded degree trees and star of chains, as well as an approximation algorithm for bipartite graphs of maximum degree at most twelve which beats the best known approximation ratios.     

Theories of International Relations: from an American Science Towards a Pluralism of Thought

The change was not only promised during Obama’s electoral campaign but urgently invoked by all the major international actors in view of the financial and economic crises: the world foreign policy has suddenly entered a new age not being yet prepared to govern globalization and its wide interdependence conditionality. Even Russia has changed tune now and President Medvedev announced vigorously a new strategy, a new policy, and a new drive for Russia. Until a few years ago, the theories of International Relations were simply an American intellectual and governance exit of the growing role of the US in the world, a kind of field of competence for the greatest power in the global economic, political, strategic and innovative sectors. The British School was an island of the core American thinking and the rest of the world mostly absent. FSU has not expressed a relevant contribution to the various schools of thinking related to the IR theories and even the Marxist political scientists did not dedicate specifically to this main research area because convinced that first it was not a real “science” but a derivative outcome from Philosophy or Political Science; secondly, for the reason of the monopoly of the power in the hands of an autocratic regime where these issues were not left to the researchers and experts but only to the institutional and military leadership. Today Russia—after having caressed and found opportunistically convenient to resume the realism doctrines of the past US almost decennial Presidency, with modest attempts to assume the great changes in international affairs intervened— has the chance to take the last train for a competitive power role, “de facto” under the unavoidable strict rules of engagement of the global governance. In the 2020–2030 the world would be completely reshape by the present metamorphosis.     

University Quality and Labour Market Outcomes in Italy

Abstract. This paper uses proxies for university quality derived from Performance Indicators to evaluate the impact of university quality on the early labour market outcome of a cohort of recent Italian graduates. Institutional research quality is found to have a negative effect on the probability that both male and female graduates will be overeducated. Additionally, research inputs are positively related to men's wages. In contrast, teaching quality does not appear to enhance students’ economic success.     

ENDOGENOUS NETWORKS IN RANDOM POPULATION GAMES

Population learning in dynamic economies traditionally has been studied in contexts where payoff landscapes are smooth. Here, dynamic population games take place over “rugged” landscapes, where agents are uncertain about payoffs from bilateral interactions. Notably, individual payoffs from playing a binary action against everyone else are uniformly distributed over [0, 1]. This random population game leads the population to adapt over time, with agents updating both actions and partners. Agents evaluate payoffs associated to networks thanks to simple statistics of the distributions of payoffs associated to all combinations of actions performed by agents out of the interaction set. Simulations show that: (1) allowing for endogenous networks implies higher average payoff compared to static networks; (2) the statistics used to evaluate payoffs affect convergence to steady-state; and (3) for statistics MIN or MAX, the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.     

Conditioning on Auxiliary Variable Means in Finite Population Inference

This paper reviews conditional properties of the mean and total estimators of a finite population when auxiliary information is available. An exact design-based conditional analysis for complex sampling designs is intractable, but an asymptotic conditional framework can be developed. Within such a framework the paper establishes sufficient conditions for conditional unbiasedness and explores conditional properties of various types of regression estimators. A sample statistic capable of indicating the presence of substantial conditional biases is proposed, and illustrated by a simulation study.