In this paper we propose an extension of the nonparametric Granger causality test, originally introduced by Diks and Panchenko [2006. A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics & Control 30, 1647-1669]. We show that the basic test statistics lacks consistency in the multivariate setting. The problem is the result of the kernel density estimator bias, which does not converge to zero at a sufficiently fast rate when the number of conditioning variables is larger than one. In order to overcome this difficulty we apply the data-sharpening method for bias reduction. We then derive the asymptotic properties of the 'sharpened' test statistics and we investigate its performance numerically. We conclude with an empirical application to the US grain market.
There is no clear-cut evidence on how the adoption of the European fiscal standards influences discretionary fiscal policies within the Member States. This study investigates that phenomenon on the example of the 2004 enlargement. The results show that the effects of the adoption of EU fiscal rules bring a statistically significant change towards more countercyclical behavior. The results are robust for different model specifications, including alternative time spans and correcting for the possible influence of the financial crises and political forces. Interestingly, the year 2004 did not have any significant impact on the change in fiscal policies in the Old Member States, suggesting that the EU entry might motivate new members to run more prudent budgetary policies.
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