The external and domestic side of macroeconomic adjustment in China

This paper sheds new light on the external and domestic dimension of China’s exchange rate policy. It presents an open economy model to analyse both dimensions of macroeconomic adjustment in China under both flexible and fixed exchange rate regimes. The model-based results indicate that persistent current account surpluses in China cannot be rationalized, under general circumstances, by the occurrence of permanent technology or labour supply shocks. As a result, the understanding of the macroeconomic adjustment process in China requires to mimic the effects of potential inefficiencies, which induce the subdued response of domestic absorption to permanent income shocks causing thereby the observed positive unconditional correlation of trade balance and output. The paper argues that these inefficiencies can be potentially seen as a by-product of the fixed exchange rate regime, and can be approximated by a stochastic tax on domestic consumption or time varying transaction cost technology related to money holdings. Our results indicate that a fixed exchange regime with financial market distortions, as defined above, might induce negative effects on GDP growth in the medium-term compared to a more flexible exchange rate regime. JEL Classification: E32, E62China, current account, DSGE modelling

The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective

In this article, we derive a new stepsize adaptation for the normalized least mean square algorithm (NLMS) by describing the task of linear acoustic echo cancellation from a Bayesian network perspective. Similar to the well-known Kalman filter equations, we model the acoustic wave propagation from the loudspeaker to the microphone by a latent state vector and define a linear observation equation (to model the relation between the state vector and the observation) as well as a linear process equation (to model the temporal progress of the state vector). Based on additional assumptions on the statistics of the random variables in observation and process equation, we apply the expectation-maximization (EM) algorithm to derive an NLMS-like filter adaptation. By exploiting the conditional independence rules for Bayesian networks, we reveal that the resulting EM-NLMS algorithm has a stepsize update equivalent to the optimal-stepsize calculation proposed by Yamamoto and Kitayama in 1982, which has been adopted in many textbooks. As main difference, the instantaneous stepsize value is estimated in the M step of the EM algorithm (instead of being approximated by artificially extending the acoustic echo path). The EM-NLMS algorithm is experimentally verified for synthesized scenarios with both, white noise and male speech as input signal.Comment: 4 pages, 1 page of reference

Alfv\'en-dynamo balance and magnetic excess in MHD turbulence

3D Magnetohydrodynamic (MHD) turbulent flows with initially magnetic and kinetic energies at equipartition spontaneously develop a magnetic excess (or residual energy), as well in numerical simulations and in the solar wind. Closure equations obtained in 1983 describe the residual spectrum as being produced by a dynamo source proportional to the total energy spectrum, balanced by a linear Alfv\'en damping term. A good agreement was found in 2005 with incompressible simulations; however, recent solar wind measurements disagree with these results. The previous dynamo-Alfv\'en theory is generalized to a family of models, leading to simple relations between residual and total energy spectra. We want to assess these models in detail against MHD simulations and solar wind data. The family of models is tested against compressible decaying MHD simulations with low Mach number, low cross-helicity, zero mean magnetic field, without or with expansion terms (EBM or expanding box model). A single dynamo-Alfv\'en model is found to describe correctly both solar wind scalings and compressible simulations without or with expansion. It is equivalent to the 1983-2005 closure equation but with critical balance of nonlinear turnover and linear Alfv\'en times, while the dynamo source term remains unchanged. The discrepancy with previous incompressible simulations is elucidated. The model predicts a linear relation between the spectral slopes of total and residual energies $m_R = -1/2 + 3/2 m_T$. Examining the solar wind data as in \cite{2013ApJ...770..125C}, our relation is found to be valid whatever the cross-helicity, even better so at high cross-helicity, with the total energy slope varying from $1.7$ to $1.55$.Comment: 7 pages, 7 figures, accepted for publication in A&

A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition

This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules leading to a unified view on known derivations as well as to new formulations for certain approaches. The generic Bayesian perspective provided in this contribution thus highlights structural differences and similarities between the analyzed approaches