The variational Bayesian approach to fitting mixture models to circular wave direction data

The emerging variational Bayesian (VB) technique for approximate Bayesian statistical inference is a nonsimulation- based and time-efficient approach. It provides a useful, practical alternative to other Bayesian statistical approaches such as Markov chain Monte Carlo–based techniques, particularly for applications involving large datasets. This article reviews the increasingly popular VB statistical approach and illustrates how it can be used to fit Gaussian mixture models to circular wave direction data. This is done by taking the straightforward approach of padding the data; this method involves adding a repeat of a complete cycle of the data to the existing dataset to obtain a dataset on the real line. The padded dataset can then be analyzed using the standard VB technique. This results in a practical, efficient approach that is also appropriate for modeling other types of circular, or directional, data such as wind direction

Money Demand in an Open Transition Economy

This article offers an internationalized view of the demand for money as applied to the Czech Republic. The traditional money demand function, consisting purely of domestic variables, is extended to include certain foreign determinants that likely affect the demand for money in a small open transition economy. In this respect, both narrow and broad money are considered. Several estimation techniques, DOLS, DGLS, the Johansen method, and ARDL, are applied to increase robustness of the results acquired. The stability of the estimates obtained is tested to study changes in the estimated relations during the transition period. Finally, estimates of the possible effects of money market disequilibria on prices and output are presented. The results suggest that international variables are significant mainly in the context of broad money demand and that liquidity gaps significantly influence prices and output dynamics.