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We propose a novel approach to investigate the synchronization of business cycles and we apply it to a Eurostat database of manufacturing industrial production time-series in the European Union (EU) over the 2000-2017 period. Our approach exploits Random Matrix Theory and extracts the latent information contained in a balanced panel data by cleaning it from possible spurious correlation. We employ this method to study the synchronization among different countries over time. Our empirical exercise tracks the evolution of the European synchronization patterns and identifies the emergence of synchronization clusters among different EU economies. We find that synchronization in the Euro Area increased during the first decade of the century and that it reached a peak during the Great Recession period. It then decreased in the aftermath of the crisis, reverting to the levels observable at the beginning of the 21st century. Second, we show that the asynchronous business cycle dynamics at the beginning of the century was structured along a East-West axis, with eastern European countries having a diverging business cycle dynamics with respect to their western partners. The recession brought about a structural transformation of business cyclesco-movements in Europe. Nowadays the divide can be identified along the North vs. South axis. This recent surge in asynchronization might be harmful for the European Union because it implies countries' heterogeneous responses to common policies.

In this paper, we investigate the temporal dynamics of correlations between sentiment indices worldwide. Employing the tools of Random Matrix Theory (RMT) and Principal Component Analysis (PCA), our paper aims to extract latent information embedded in the interactions between economic and business sentiment indices around the world. We find that: (i) The dynamics of the sentiment indices across countries can be well explained by the evolution of a single factor (the "market mode"), (ii) during most periods, some groups of countries exhibit sentiment dynamics less associated with (or divergent from) the market mode, while (iii) during the financial crisis, no country or group of countries has been able to escape the market mode, which accounts for almost all movements in the indices. We argue that strong "global" information signals, like the collapse of the US housing market in 2007, can lead to a homogenization of the expectation structure around the world, as such information can provide a coordination signal for a global phase of low confidence.

We investigate the structural dependencies in the bank-firm credit market of Spain under a multilayer network perspective. In particular, the original bipartite network is decomposed into different layers representing different industrial sectors. We then study the correlations between layers based on normalized measures of overlaps of links and weights of banks between layers. To assess the statistical significance of such correlations, we compare the observed values with the expected ones obtained from random graph models specifying only global constraints, i.e. the total degree or the total strength in single layers, and from configuration models capturing the intrinsic heterogeneity in the local constraints like the observed degree sequence and/or strength sequence in single layers. We find that, first, the raw dependencies between layers of the observed network are highly heterogeneous. Second, when evaluated against the null models, on the one hand, the rescaled correlations after filtering out the effects of the global constraints typically display no significant difference to the observed correlations. In addition, in the binary version, almost all correlations are still present after subtracting the effects of the observed degree sequences in all layers. On the other hand, the observed correlations are partially explained by the local constraints maintained in the weighted configuration models. All in all, comparing the observed network with all referenced null models, we find that the multilayer credit network under scrutiny has a significant, non-random structure of correlations that cannot be explained by more primitive network properties alone. In the binary case, such a non-random structure is, for instance, typically observed in the pairs of layers that have high levels of overlaps and correlations. In contrast, in the weighted case, patterns are found in different pairs of layers that have various levels of overlaps and correlations.

We study the structural correlations in the Italian overnight money market over the period 1999-2010. We show that the structural correlations vary across different versions of the network. Moreover, we employ different configuration models and examine whether higher-level characteristics of the observed network can be statistically reconstructed by maximizing the entropy of a randomized ensemble of networks restricted only by the lower-order features of the observed network. We find that often many of the high order correlations in the observed network can be considered emergent from the information embedded in the degree sequence in the binary version and in both the degree and strength sequences in the weighted version. However, this information is not enough to allow the models to account for all the patterns in the observed higher order structural correlations. In particular, one of the main features of the observed network that remains unexplained is the abnormally high level of weighted clustering in the years preceding the crisis, i.e. the huge increase in various indirect exposures generated via more intensive interbank credit links.

This study explores the effect of external engagements on innovationoutputs of firms using a cross-sectional survey data set on Vietnamese privatemanufacturing SMEs conducted in 2009.For theoretical framework, we use the the knowledge production functionmodel (PKF) (Griliches, 1979, 1990) and the network models of innovation(NMI) (OECD, 2005; Osamu, 2008).Based on PKF and NMI models, we found that there was generally apositive impact of international engagements (export, import of equipment/machinery, and the supports from foreign donors/NGOs) and other domesticengagements (subcontracting, purchasing outside business service, and beingmember of business association activities) on innovation outputs. This may bebecause externally engaged firms invested more in innovation, and/or becausethese firms were able to learn from their worldwide partners or access to a widersource of knowledge flows.Our study gives some theoretical and managerial implications. Fortheoretical implications, the findings support KPF and NMI. Firm innovationbecomes open and interconnected to external factors. For managerialimplication, increasing external engagements is a good strategy for firms toovercome their weaknesses of limited internal resources. Increasing externalengagements can also force firms to innovate more.