97 research outputs found

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

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    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

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    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

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    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

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    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

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

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    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
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