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We summarize the accomplishments of a multidisciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We study the replacement of orthographic transcriptions by images and/or translated text in a well-resourced language to help unsuper-vised discovery from raw speech.
This paper provides direct evidence that learning about demand is an important driver of firms' dynamics. We present a model of Bayesian learning in which firms are uncertain about their idiosyncratic demand in each of the markets they serve, and update their beliefs as noisy information arrives. Firms are predicted to update more their beliefs to a given demand shock, the younger they are.
We develop a model of cross-border acquisitions in which the foreign acquirer's ownership choice reflects a trade-off between easing the target's credit constraints and the costs of operating in an environment with weak institutions. Data on domestic and foreign acquisitions in emerging markets over the period 1990-2007 support the model predictions.