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Given that existing implementations of synthetic control methods are plagued by serious weaknesses, new methods are offered for calculating synthetic control units. In particular, it is shown how to detect and handle important special cases that have yet to be addressed. Numerical methods for fast and reliably solving the nested optimization associated with the standard case are also discussed. An open source implementation of the presented methods is provided with the R package MSCMT, which can also be applied to generalizations of ‘standard’ synthetic control methods.

We present a model of opinion formation where individuals repeatedly engage in discussion and update their opinion in a social network similarly to the DeGroot model. Abstracting from the standard assumption that individuals always report their opinion truthfully, agents in our model may state an opinion that differs from their true opinion. The incentive to do so is induced by agents' preferences for conformity. We model opinion formation as a dynamic process and identify conditions for convergence to a consensus. Studying the consensus in detail, we show that an agent's social influence on the consensus opinion is increasing in network centrality and decreasing in the level of conformity. Thus, lower conformity fosters opinion leadership. Moreover, assuming that the initial opinion is a noisy signal about some true state of the world, we study how conformity affects the efficiency of information aggregation or the ``wisdom'' of the society. We show that the society becomes wiser, in the sense of a smaller mean squared error of their estimate, if players who are well informed (relative to their network importance) are less conform, while uninformed players (relative to their network importance) conform more with their neighbors.

Using 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 US and UK are responsible for a large fraction of the spillovers since the financial crisis, while the remaining countries are all net receivers of policy uncertainty shocks during and after this period.

We introduce a model for stock prices consisting of a fundamental price process and a news impact curve, which allows for either overreaction, underreaction, or correct response to changes of the fundamental value. We further develop statistics based on OHLC data, which separately measure upside and downside overreaction. The distribution of these statistics under the hypothesis of correct response and fundamental prices following Brownian motions is used to derive tests for upside and downside overreaction. We show that more realistic and frequently used fundamental price processes with correct response leave the distribution of the test statistics widely unaffected or lead to conservative tests. Empirical application to different stock markets provides strong evidence for intraday overreaction, particularly to bad news. The economic significance of the discrimination induced by the proposed statistics is further illustrated by analyzing the performance of a simple buy on bad news strategy.

We present a model of opinion formation where individuals repeatedly engage in discussion and update their opinion in a social network similarly to the DeGroot model. Abstracting from the standard assumption that individuals always report their opinion truthfully, agents in our model interact strategically in the discussion such that their stated opinion can differ from their true opinion. The incentive to do so is induced by agents' preferences for conformity. Highly conforming agents will state an opinion which is close to their neighbors' while agents with low level of conformity may be honest or even overstate their opinion. We model opinion formation as a dynamic process and identify conditions for convergence to consensus. Studying the consensus in detail, we show that an agent's social influence on the consensus opinion is increasing in network centrality and decreasing in the level of conformity. Thus, lower conformity fosters opinion leadership. Moreover, assuming that the initial opinion is a noisy signal about some true state of the world, we consider the mean squared error of the consensus as an estimator for the true state of the world. We show that a society is "wise", i.e. the mean squared error is smaller, if players who are well informed are less conform, while uninformed players conform more with their neighbors.