Endogenous Business Cycles with Consumption Externalities

Empirical evidences tell us that in the recent years the expansion period is increased with reduction of the contraction period in the U.S. business cycles. Moreover, the business cycles in the United States also show the trend to be moderated with recent economic growth induced and supported by high technologies and their industries. We study endogenous business cycles by a modified synthesized endogenous business cycles model “in which expansions are neoclassical growth periods driven by productivity improvements and capital accumulation, while downturns are the results of Keynesian contractions in aggregate demand†(Francois and Lloyd-Ellis, 2002), with consumption externalities. By considering consumption externalities, the endogenized business cycles will be more likely to happen, the optimal consumption level will be higher, the technology growth rate will be bigger, the length of expansion will be longer and the length of contraction will be shorter. All of these results will lead to a faster and longer economic growth and smoother cycles. These theoretical results are significantly different from those in circumstances without the consumption externalities Francois and Lloyd-Ellis (2002) obtained, and are strongly supported by the data from the United States in the different periods.Endogenous Business Cycle, Consumption Externality, Endogenous Growth

Connection Discovery using Shared Images by Gaussian Relational Topic Model

Social graphs, representing online friendships among users, are one of the fundamental types of data for many applications, such as recommendation, virality prediction and marketing in social media. However, this data may be unavailable due to the privacy concerns of users, or kept private by social network operators, which makes such applications difficult. Inferring user interests and discovering user connections through their shared multimedia content has attracted more and more attention in recent years. This paper proposes a Gaussian relational topic model for connection discovery using user shared images in social media. The proposed model not only models user interests as latent variables through their shared images, but also considers the connections between users as a result of their shared images. It explicitly relates user shared images to user connections in a hierarchical, systematic and supervisory way and provides an end-to-end solution for the problem. This paper also derives efficient variational inference and learning algorithms for the posterior of the latent variables and model parameters. It is demonstrated through experiments with over 200k images from Flickr that the proposed method significantly outperforms the methods in previous works.Comment: IEEE International Conference on Big Data 201