We apply semiparametric efficient estimation procedures for a seemingly unrelated regression model where the multivariate error density is elliptically symmetric to study the efficiency of the foreign exchange market. We consider both cointegrating regressions and standard stationary...

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the...

Statistical flowgraphs represent multistate semi-Markov processes using integral transforms of transition time distributions between adjacent states; these are combinedalgebraically and inverted to derive parametric estimates for first passage time distributions between nonadjacent states. This...

The stylized fact of time-varying volatility in financial series is commonly accepted amongst scholars as well as practitioners. The GARCH model has been exceptionally successful in this area. Our approach, the minimally cross-entropic conditional density (MCECD) model, is a generalization of...

Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new methods for conditional distribution estimation. The first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density...