Macroscopic quantum superpositon states of two-component Bose-Einstein condensates

We examine a two-component Bose-Einstein condensate in a double-well potential. We propose a model for the creation of many-particle macroscopic quantum superposition states. The effect of dissipation on the formation of these states is also investigated with the Monte-Carlo wavefunction technique.Comment: Accepted for pubilcation in PRA. 8 pages, 15 figure

Monetary policy rules and a normative approach to the central bank’s objective function

This study attempts to explain in an understandable manner that the central bank’s effort to keep inflation low is not an end in itself, but ultimately serves the interests of social welfare. We attempt to substantiate this argument on the basis of economic theory, based on the logic of New Keynesian models, by describing loss functions that contain welfare relevant variables and interest rules that minimise them. By using this framework, we point out that – taking into account the limits of measurability, learning and potentially non-rational expectations – decision-making rules that give considerable weight to a departure from the inflation target and take into account real economy considerations generally perform well in terms of welfare and may be considered robust in New Keynesian-type models with forward looking agents. Finally, we argue that through the strategy of inflation targeting the normative implications of the above framework can be put into practice.New_keynesian model, inflation targeting, normative approach.

Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators

We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences. Rather than plugging a consistent density estimate (which requires $k \to \infty$ as the sample size $n \to \infty$) into the functional of interest, the estimators we consider fix k and perform a bias correction. This is more efficient computationally, and, as we show in certain cases, statistically, leading to faster convergence rates. Our framework unifies several previous estimators, for most of which ours are the first finite sample guarantees.Comment: 16 pages, 0 figure