A macroeconomic assessment of the European Monetary Union. EUMA Paper Vol. 7, No. 5, April 2010

Since the inception of Euro in 1999, a single currency and the Economic and Monetary Union (EMU) have past more than ten years. By and large, stepping into EMU represents one of the key aspects of EU’s successful integration. For most of its short life, the European Union has been driven mainly by the goal of economic integration. From a limited experiment in economic cooperation during the early 1950s, boarded in the 1960s to become a custom union, wrestled during the 1970s with attempts to build common economic policies and exchange rate stability, focused on completing the single market during the late 1980s, to the Economic and Monetary union and a single currency at the present1, the European Union has followed a tortuous path. The paper starts with the effectiveness of EU’s monetary policy after the birth of Euro to explore the complex relationship between monetary policy and economic operation within the European Union

Nonparametric spectral analysis with applications to seizure characterization using EEG time series

Understanding the seizure initiation process and its propagation pattern(s) is a critical task in epilepsy research. Characteristics of the pre-seizure electroencephalograms (EEGs) such as oscillating powers and high-frequency activities are believed to be indicative of the seizure onset and spread patterns. In this article, we analyze epileptic EEG time series using nonparametric spectral estimation methods to extract information on seizure-specific power and characteristic frequency [or frequency band(s)]. Because the EEGs may become nonstationary before seizure events, we develop methods for both stationary and local stationary processes. Based on penalized Whittle likelihood, we propose a direct generalized maximum likelihood (GML) and generalized approximate cross-validation (GACV) methods to estimate smoothing parameters in both smoothing spline spectrum estimation of a stationary process and smoothing spline ANOVA time-varying spectrum estimation of a locally stationary process. We also propose permutation methods to test if a locally stationary process is stationary. Extensive simulations indicate that the proposed direct methods, especially the direct GML, are stable and perform better than other existing methods. We apply the proposed methods to the intracranial electroencephalograms (IEEGs) of an epileptic patient to gain insights into the seizure generation process.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS185 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org