3 resultados para ANCESTRAL STATE RECONSTRUCTION

em Consorci de Serveis Universitaris de Catalunya (CSUC), Spain


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The defaults of Philip II have attained mythical status as the origin of sovereign debt crises. Four times during his reign the king failed to honor his debts and had to renegotiate borrowing contracts. In this paper, we reassess the fiscal position of Habsburg Spain. New archival evidence allows us to derive comprehensive estimates of debt and revenue. These show that primary surpluses were sufficient to make the king's debt sustainable in most scenarios. Spain's debt burden was manageable up to the 1580s, and its fiscal position only deteriorated for good after the defeat of the "Invincible Armada." We also estimate fiscal policy reaction functions, and show that Spain under the Habsburgs was at least as "responsible" as the US in the 20th century or as Britain in the 18th century. Our results suggest that the outcome of uncertain events such as wars may influence on a history of default more than strict adherence to fiscal rules.

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The defaults of Philip II have attained mythical status as the origin of sovereigndebt crises. We reassess the fiscal position of Habsburg Castile, derivingcomprehensive estimates of revenue, debt, and expenditure from new archivaldata. The king s debts were sustainable. Primary surpluses were large and rising.Debt-to-revenue ratios remained broadly unchanged during Philip s reign.Castilian finances in the sixteenth century compare favorably with those of otherearly modern fiscal states at the height of their imperial ambitions, includingBritain. The defaults of Philip II therefore reflected short-term liquidity crises,and were not a sign of unsustainable debts.

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A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.