Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. iii Terms of use: Documents in AbstractUsing the Baker et al. (2013) index of policy uncertainty for six developed countries, this paper estimates spillovers of policy uncertainty. We find that spillovers account for slightly more than one-fourth of the dynamics of policy uncertainty in these countries, with this share rising to one-half during the financial crisis. The United States and United Kingdom are responsible for a large fraction of the spillovers since the financial crisis, while Canada and the remaining countries are all net receivers of policy uncertainty shocks during and after this period. JEL classification: C3, D80, F42 Bank classification: Econometric and statistical methods RésuméÀ l'aide de l'indice d'incertitude sur la politique économique établi par Baker et autres (2013), les auteurs estiment les retombées de l'incertitude au sujet de la politique économique dans le cas de six pays développés. Ils constatent que ces effets d'entraînement contribuent pour un peu plus du quart à la dynamique de l'incertitude sur la politique économique dans ces pays, proportion qui a grimpé à 50 % durant la crise financière. Depuis cette dernière, une grande part des retombées de l'incertitude émanent des États-Unis et du Royaume-Uni. Quant au Canada et aux autres pays, ils ont surtout subi les chocs d'incertitude liés à la politique économique pendant et après cette période.
We study a dynamic model of opinion formation in social networks. In our model, boundedly rational agents update opinions by averaging over their neighbors' expressed opinions, but may misrepresent their own opinion by conforming or counter-conforming with their neighbors. We show that an agent's social influence on the long-run group opinion is increasing in network centrality and decreasing in conformity. For efficiency of information aggregation ("wisdom"), misrepresentation of opinions need not undermine wisdom. Given the network, we provide the optimal distribution of conformity levels in the society and show which agents should be more conforming in order to increase wisdom.
Recently, Abadie et al. (Am J Polit Sci 59:495–510, 2015) have expanded synthetic control methods by the so-called cross-validation technique. We find that their results are not being reproduced when alternative software packages are used or when the variables’ ordering within the dataset is changed. We show that this failure stems from the cross-validation technique relying on non-uniquely defined predictor weights. While the amount of the resulting ambiguity is negligible for the main application of Abadie et al. (Am J Polit Sci 59:495–510, 2015), we find it to be substantial for several of their robustness analyses. Applying well-defined, standard synthetic control methods reveals that the authors’ results are particularly driven by a specific control country, the USA.
Diebold and Yilmaz (Economic Journal 2009; 119; 158-171) introduce the spillover index to measure linkages between international financial markets. As their index depends on the ordering of the variables in the underlying VAR model, they check robustness by computing the index for a small number of randomly chosen permutations, stating that it was impossible to explore the huge number of renumerations. Building on a new divide-and-conquer strategy, we provide an algorithm for swiftly calculating the spillover index's maximum and minimum over all renumerations. Using this new algorithm, we find that the true range of the spillover index can be up to three times as large as estimated by Diebold and Yilmaz. EXPLORING ALL ORDERINGS FOR SPILLOVERS 173 Building on a divide-and-conquer strategy, we provide an algorithm which allows timely calculation of the range of the spillover index even for large models. Applying it to the volatility data of Diebold and Yilmaz (2009), we show that the above-mentioned estimation of the range by randomly choosing permutations seriously underestimates the impact of the VAR ordering on the spillover index, with the estimated range sometimes comprising only one third of the true range. NOTATION AND METHODOLOGYLet us start by replicating, for the reader's convenience, the notation and methodology introduced in Diebold and Yilmaz (2009). To this end, we start with an N -dimensional VAR(p) model, Y t D (6) 4 To save space, we will restrict our attention to the maximum in equation (4), the modifications for computing the minimum being obvious. Analogously, it is also possible to compute the average of the spillover index over all variable reorderings. 5 The use of the more general formula equation (5) will become clear later. 6 The proposition's proof can be found in the Appendix. 7 The structural equivalence of the optimization problems is only true because we generalized equation (4) to equation (5). 8 The implemented algorithm uses N 1 D b N 2 c; other choices of N 1 will of course deliver the same results, but typically need more computation time.
JEL classification: C72 D83 D85 Z13 Keywords: Opinion leadership Wisdom of crowds Consensus Social networks Conformity Eigenvector centrality a b s t r a c tWe study a dynamic model of opinion formation in social networks. In our model, boundedly rational agents update opinions by averaging over their neighbors' expressed opinions, but may misrepresent their own opinion by conforming or counter-conforming with their neighbors. We show that an agent's social influence on the long-run group opinion is increasing in network centrality and decreasing in conformity. Concerning efficiency of information aggregation or "wisdom" of the society, it turns out that misrepresentation of opinions need not undermine wisdom, but may even enhance it. Given the network, we provide the optimal distribution of conformity levels in the society and show which agents should be more conforming in order to increase wisdom.
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