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on Network Economics |
| By: | Chih-Sheng Hsieh (National Taiwan University); Michael König (Vrije Universiteit Amsterdam); Xiaodong Liu (University of Colorado Boulder); Christian Zimmermann (Federal Reserve Bank of St. Louis) |
| Abstract: | This paper studies the impact of collaboration on research output. First, we build a micro founded model for scientific knowledge production, where collaboration between researchers is represented by a bipartite network. The equilibrium of the game incorporates both the complementarity effect between collaborating researchers and the substitutability effect between concurrent projects of the same researcher. Next, we develop a Bayesian MCMC procedure to estimate the structural parameters, taking into account the endogenous matching of researchers and projects. Finally, we illustrate the empirical relevance of the model by analyzing the coauthorship network of economists registered in the RePEc Author Service. |
| Keywords: | bipartite networks, coauthorship networks, research collaboration, spillovers, economics of science |
| JEL: | C31 C72 D85 L14 |
| Date: | 2020–09–08 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20200056&r=all |
| By: | Haelim Anderson; Selman Erol; Guillermo Ordoñez |
| Abstract: | Central banks provide public liquidity to traditional (regulated) banks with the intention of stabilizing the financial system. Shadow banks are not regulated, yet they indirectly access such liquidity through the interbank system. We build a model that shows how public liquidity provision may change the linkages between traditional and shadow banks, increasing systemic risk through three channels: reducing aggregate liquidity, expanding fragile short-term borrowing, and crowding out of private cross-bank insurance. We show that the creation of the Federal Reserve System and the provision of public liquidity changed the structure and nature of the U.S. interbank network in ways that are consistent with the model and its implications. We provide empirical evidence by constructing unique data on balance sheets and detailed disaggregated information on payments and funding connections in Virginia. |
| JEL: | D53 D85 E02 E44 G11 G21 G23 N21 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27721&r=all |
| By: | Andrin Pelican; Bryan S. Graham |
| Abstract: | We introduce a test for whether agents' preferences over network structure are interdependent. Interdependent preferences induce strategic behavior since the optimal set of links directed by agent i will vary with the configuration of links directed by other agents. Our model also incorporates agent-specific in- and out-degree heterogeneity and homophily on observable agent attributes. This introduces 2N+K^2 nuisance parameters (N is number of agents in the network and K the number of possible agent attribute configurations). Under the null equilibrium is unique, but our hypothesis is nevertheless a composite one as the degree heterogeneity and homophily nuisance parameters may range freely across their parameter space. Under the alternative our model is incomplete; there may be multiple equilibrium network configurations and our test is agnostic about which one is selected. Motivated by size control, and exploiting the exponential family structure of our model under the null, we restrict ourselves to conditional tests. We characterize the exact null distribution of a family of conditional tests and introduce a novel Markov Chain Monte Carlo (MCMC) algorithm for simulating this distribution. We also characterize the locally best test. The form of this test depends upon the gradient of the likelihood with respect to the strategic interaction parameter in the neighborhood of the null. Remarkably, this gradient, and consequently the form of the locally best test statistic, does not depend on how an equilibrium is selected. Exploiting this lack of dependence, we outline a feasible version of the locally best test. We present two illustrative applications. First, we test for whether nations behave strategically when choosing locations for overseas diplomatic missions. Second, we test for whether firms prefer to sell to firms with richer customer bases (i.e., whether firms value “indirect customers”). Some Monte Carlo experiments explore the size and power properties of our test in practice. |
| JEL: | C31 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27793&r=all |
| By: | Pablo Fajgelbaum (Princeton University and NBER); Amit Khandelwal (Columbia GSB and NBER); Wookun Kim (Southern Methodist University); Cristiano Mantovani (Universitat Pompeu Fabra); Edouard Schaal (CREI, Universitat Pompeu Fabra, Barcelona GSE and CEPR) |
| Abstract: | We study optimal dynamic lockdowns against Covid-19 within a commuting network. Our framework integrates canonical spatial epidemiology and trade models, and is applied to cities with varying initial viral spread: Seoul, Daegu and NYC-Metro. Spatial lockdowns achieve substantially smaller income losses than uniform lockdowns, and are not easily approximated by simple centrality-based rules. In NYM and Daegu—with large initial shocks—the optimal lockdown restricts inflows to central districts before gradual relaxation, while in Seoul it imposes low temporal but large spatial variation. Actual commuting responses were too weak in central locations in Daegu and NYM, and too strong across Seoul. |
| JEL: | R38 R4 C6 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:smu:ecowpa:2010&r=all |
| By: | Brancati, Emanuele (University of Rome, La Sapienza); Minetti , Raoul (Michigan State University, Department of Economics); Zhu, Susan Chun (Michigan State University, Department of Economics) |
| Abstract: | Disruptions of the production network, such as that triggered by the 2020 global crisis, can spill over to firms’ financing and investment processes. This paper studies the role of the production network in the nexus between finance and investment in innovation. Using matched firm-bank data on 25,000 Italian businesses over the 2011-2017 period, we find that firms’ participation in supply chains significantly attenuates the negative effect of bank credit constraints on innovation. A disruption of 25% of the supply chain linkages is predicted to magnify the impact of credit constraints on innovation by about 17%. The support of supply chains to credit constrained innovators reflects not only liquidity pooling in supply chains but also the substitution of liquidity-intensive innovations with transfers of knowledge along R&D-oriented chains. The support fails however to materialize for radical innovations. |
| Keywords: | Banks; Financial Constraints; Innovation; Supply Chains |
| JEL: | G21 O30 |
| Date: | 2020–09–08 |
| URL: | http://d.repec.org/n?u=RePEc:ris:msuecw:2020_013&r=all |
| By: | Andrea Calef (University of East Anglia) |
| Abstract: | In this paper I study the extent to which the nexus between concentration and interbank linkages affects financial stability, using data for a sample of 19,689 banks in 69 countries from 1995 to 2014. I find that high levels of interbank exposures decrease the probability of observing a systemic banking crisis, when the banking system is either highly concentrated or fragmented. The relationship between concentration and stability is found to be non-monotonic, as predicted by Martinez-Miera & Repullo (2010), although not U-shaped. |
| Keywords: | banking crisis, systemic risk, market structure; interbank linkages, network, contagion. |
| JEL: | G01 G21 G28 |
| Date: | 2020–01–15 |
| URL: | http://d.repec.org/n?u=RePEc:uea:ueaeco:2020-02&r=all |
| By: | Chung, Jaewon; Bridgeford, Eric; Arroyo, Jesus; Pedigo, Benjamin D.; Saad-Eldin, Ali; Gopalakrishnan, Vivek; Xiang, Liang; Priebe, Carey E.; Vogelstein, Joshua T. |
| Abstract: | The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics. |
| Date: | 2020–08–12 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:ek4n3&r=all |
| By: | Huremović, Kenan; Ozkes, Ali |
| Abstract: | We introduce a model of polarization in networks as a unifying setting for the measurement of polarization that covers a wide range of applications. We consider a substantially general setup for this purpose: node-and edge-weighted, undirected, and connected networks. We generalize the axiomatic characterization of Esteban and Ray (1994) and show that only a particular instance within this class can be used justifiably to measure polarization in networks. |
| Keywords: | measurement, networks, polarization |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus055:7742&r=all |
| By: | Fernando Moraes; Rodrigo De-Losso |
| Abstract: | The Factor Zoo phenomenon calls for answers as to which risk factors are in fact capable of providing independent information on the cross-section of expected excess returns, while considering that asset-pricing literature has produced hundreds of candidates. In this paper, we propose a new methodology to reduce risk factor predictor dimensions by selecting the key component (most central element) of their precision matrix. Our approach yields a significant shrinkage in the original set of risk factors, enables investigations on different regions of the risk factor covariance matrix, and requires only a swift algorithm for implementation. Our findings lead to sparse models that pose higher average in samples !" and lower root mean square out of sample error than those attained with classic models, in addition to specific alternative methods documented by Factor Zoo-related research papers. We base our methodology on the CRSP monthly stock return dataset in the time frame ranging from January 1981 to December 2016, in addition to the 51 risk factors suggested by Kozak, Nagel, and Santosh (2020). |
| Keywords: | Risk factors; factor zoo; graph lasso; network analysis |
| JEL: | G12 C55 D85 |
| Date: | 2020–09–15 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2020wpecon17&r=all |