develop disproportionately faster in countries with more developed nancial markets. We nd this to be true in a large sample of countries over the 1980s. We show this result is unlikely to be driven by omitted variables, outliers, or reverse causality. (JEL O4, F3, G1) A large literature, dating at least

computing the posterior model probabilities is hard and the number of models is very large (often greater than 1011). We argue that the standard Bayesian formalism is unsatisfactory and we propose an alternative Bayesian approach that, we contend, takes full account of the true model uncertainty byaveraging

bisimilarity. One model has not been integrated into this framework: the causal trees of Darondeau and Degano. Here we fill this gap. In particular, we show that there is an adjunction from causal trees to event structures, which we bring to light via a mediating model, that of event trees. Further, we achieve

if the published trends are representative of true regional to global averages 55 . The Indian Ocean Experiment 7 used a variety of chemical, physical and optical measurements to convincingly demonstrate that ABCs can lead to dimming as large as 5-10% Global climate effects The TOA BC forcing implies that BC has

bisimilarity. One model has not been integrated into this framework: the causal trees of Darondeau and Degano. Here we fill this gap. In particular, we show that there is an adjunction from causal trees to event structures, which we bring to light via a mediating model, that of event trees. Further, we achieve

that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network. 1

. In this paper, we demonstrate that (i) BGC in time domain may not necessarily disclose true causality. (ii) Due to the use of the transfer function or its inverse matrix and partial information of the multivariate linear regression model, both of spectral BGC and conditional spectral BGC have shortcomings and

Three experiments were conducted within the framework of correspondent inference theory. In each of the experiments the subjects were instructed to estimate the "trueJ ' attitude of a target person after having either read or listened to a speech by him expressing opinions on a

We present a method for formalising the behaviour of simple deterministic devices and protocols in a way that makes explicit the causal dependencies amongst the component elements, thereby allowing true causal (as opposed to purely temporal) reasoning. Our intention is to handle such systems

, nonlinearities in the data-generating process are in fact a blessing rather than a curse, as they typically provide information on the underlying causal system and allow more aspects of the true data-generating mechanisms to be identified. In addition to theoretical results we show simulations and some simple