Committing to Economic Openness in Ireland: The Importance of Domestic Institutional Capabilities

This chapter sets out to explain the factors behind Ireland's exceptional period of economic growth from the early 1990s to the mid 2000s. It suggests that an unbending commitment to economic openness and an on-going effort to establish quality domestic institutions were the main drivers of the so-called ‘Celtic tiger’ phenomenon. The commitment to economic openness manifested itself in the relentless search for inward investment and a willingness to accept deep forms of European integration. Building domestic institutional capabilities involved adopting new-classical macroeconomic policies, creating a robust system of social partnership and reforming the educational system. The two factors positively interacted with each other to create dynamic effects.

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.