Inference for circular distributions and processes

One approach to defining models for circular data and processes has been to take a standard Euclidean model and to wrap it around the circle. This generates rich families of circular models but creates difficulties for inference. Using data augmentation ideas which have previously been applied to this problem in the framework of an EM algorithm, we demonstrate the power and flexibility of Markov chain Monte Carlo methods to fit such classes of models to circular data. The precision of the technique is confirmed through simulated examples, and then applications are given to multivariate and time series data of wind directions.     

PAY, TECHNOLOGY, AND THE COST OF WORKER ABSENCE

Conventional studies of absenteeism concentrate on labor supply. An equilibrium approach, however, establishes that the shadow cost of absenteeism varies across firms that operate different technologies. Using an unusual employee/employer matched data set from France, which records both individual worker absenteeism and information about technology, we show that firms operating just-in-time technology have higher shadow costs of absence than firms that do not. The estimates are used to calculate the economy-wide cost of absence, which turns out to be very low. ( JEL J22, J31, J41)