Too Much to Lose, or More to Gain? Should Sweden Join the Euro?

This paper considers the costs and benefits of Sweden joining the European Economic and Monetary Union (EMU). We pay particular attention to the costs of abandoning the krona in terms of a loss of monetary policy independence. For this purpose, we apply a cointegrated VAR framework to examine the degree of monetary independence that the Sveriges Riksbank enjoys. Our results suggest that Sweden has in fact relatively little to lose from joining EMU, at least in terms of monetary independence. We complement our analysis by looking into other criteria affecting the cost-benefit calculus of monetary integration, which, by and large, support our positive assessment of Swedish EMU membership

Chinese Monetary Policy and the Dollar Peg

This paper investigates to what extent Chinese monetary policy is constrained by the dollar peg. To this end, we use a cointegration framework to examine whether Chinese interest rates are driven by the Fed's policy. In a second step, we estimate a monetary model for China, in which we include also other monetary policy tools besides the central bank interest rate, namely reserve requirement ratios and open market operations. Our results suggest China has been relatively successful in isolating its monetary policy from the US policy and that the interest rate tool has not been effectively made use of. We therefore conclude that by employing capital controls and relying on other instruments than the interest rate China has been able to exert relatively autonomous monetary policy

Simulating properties of the likelihood ratio test for a unit root in an explosive second order autoregression

This paper provides a means of accurately simulating explosive autoregressive processes, and uses this method to analyse the distribution of the likelihood ratio test statistic for an explosive second order autoregressive process. Nielsen (2001) has shown that for the asymptotic distribution of the likelihood ratio unit root test statistic in a higher order autoregressive model, the assumption that the remaining roots are stationary is unnecessary, and as such the approximating asymptotic distribution for the test in the difference stationary region is valid in the explosive region also. However, simulations of statistics in the explosive region are beset by the magnitude of the numbers involved, which cause numerical inaccuracies, and this has previously constituted a bar on supporting asymptotic results by means of simulation, and analysing the finite sample properties of tests in the explosive region.

Measurement of competitive balance in professional team sports using the Normalized Concentration Ratio

Competitive balance is an important concept in professional team sports; its measurement is, therefore, a critical issue. One of the most widely used indices, which was introduced for the estimation of seasonal competitive balance is the Concentration Ratio, which is a relatively simple index and measures the extent to which a league is dominated by a particular number of teams. However, it is shown that both the total number of league teams and the number of dominant teams under examination affects the index's boundaries, which results in a misleading interpretation concerning the level of competitive balance. Thus, we introduce the Normalized Concentration Ratio for the study of competitive balance across leagues or seasons.competitive balance, concentration ratio, professional sports, sport league

Forecasting with social media: evidence from Tweets on soccer matches

Social media is now used as a forecasting tool by a variety of firms and agencies. But how useful are such data in forecasting outcomes? Can social media add any in- formation to that produced by a prediction/betting market? We source 13.8m posts from Twitter, and combine them with contemporaneous Betfair betting prices, to fore- cast the outcomes of English Premier League soccer matches as they unfold. Using a micro-blogging dictionary to analyse the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards

Whatever it takes: rivalry and unethical behavior

This research investigates the link between rivalry and unethical behavior. We propose that people will engage in greater unethical behavior when competing against their rivals than when competing against non-rival competitors. Across a series of experiments and an archival study, we find that rivalry is associated with increased use of deception, unsportsmanlike behavior, willingness to employ unethical negotiation tactics, and misreporting of performance. We also explore the psychological underpinnings of rivalry, which help to illuminate how it differs from general competition, and why it increases unethical behavior. Rivalry as compared to non-rival competition was associated with increased status concerns, contingency of self-worth, and performance goals; mediation analyses revealed that performance goals played the biggest role in explaining why rivalry promoted greater unethicality. Lastly, we find that merely thinking about a rival can be enough to promote greater unethical behavior, even in domains unrelated to the rivalry. These findings highlight the importance of rivalry as a widespread, powerful, yet largely unstudied phenomenon with significant organizational implications. Further, the results help to inform when and why unethical behavior occurs within organizations, and demonstrate that the effects of competition are dependent upon relationships and prior interactions

The wisdom of amateur crowds: evidence from an online community of sports tipsters

We analyse the accuracy of crowd forecasts produced on Oddsportal, an online community of amateur sports tipsters. Tipsters in this community are ranked according to the betting return on their tips, but there are no prizes for accuracy. Nevertheless, we find that aggregated tips in this community contain information not in betting prices. A strategy of betting when a majority predict an outcome produces average returns of 1.317% for 68,339 events. The accuracy of these forecasts stems from the wisdom of the whole crowd, as selecting sections of the crowd based on experience or past forecast accuracy does not improve betting returns

Social Pressure or Rational Reactions to Incentives? A Historical Analysis of Reasons for Referee Bias in the Spanish Football

A relevant question in social science is whether cognitive bias can be instigated by social pressure or is it just a rational reaction to incentives in place. Sport, and association football in particular, offers settings in which to gain insights into this question. In this paper we estimate the determinants of the length of time between referee appointments in Spanish soccer as a function of referee decisions in favour of the home and away team in the most recent match by means of a deep-learning model. This approach allows us to capture all interactions among a high-dimensional set of variables without the necessity of specifying them beforehand. Furthermore, deep-learning models are nowadays the state of the art among the predicting models which are needed and here used for estimating effects of a cause. We do not find strong evidence of an incentive scheme that counteracts well-known home referee biases. Our results also suggest that referees are incentivised to deliver a moderate amount of surprise in the outcome of the game what is consistent with the objective function of consumers and tournament organisers

Polls to probabilities: comparing prediction markets and opinion polls

Forecasting election outcomes is a hugely popular activity, and not without reason: outcomes can have significant economic impacts, for example on stock prices. As such, it is economically important, as well as of academic interest, to determine the forecasting methods that have historically performed best. However, forecasts are often incompatible, as some are in terms of vote shares, and others are probabilistic outcome forecasts. In this paper we set out an empirical method for transforming opinion poll vote shares into probabilistic forecasts, and then evaluate the performance of prediction markets and opinion polls. We compare along two dimensions: bias and precision. We find that converted opinion polls perform well in terms of bias, and prediction markets on precision

Going with your gut: the (in)accuracy of forecast revisions in a football score prediction game

This paper studies 150 individuals who each chose to forecast the outcome of 380 fixed events, namely all football matches during the 2017/18 season of the English Premier League. The focus is on whether revisions to these forecasts before the matches began improved the likelihood of predicting correct scorelines and results. Against what theory might expect, we show how these revisions tended towards significantly worse forecasting performance, suggesting that individuals should have stuck with their initial judgements, or their 'gut instincts'. This result is robust to both differences in the average forecasting ability of individuals and the predictability of matches. We find evidence this is because revisions to the forecast number of goals scored in football matches are generally excessive, especially when these forecasts were increased rather than decreased