We consider the effects of replacing least squares learning by stochastic gradient learning in the multivariate "Cobweb" model. Are the stability conditions altered? For this model, we show global convergence of stochastic gradient learning to the unique rational expectations equilibrium...

We study asset allocation when the conditional moments of returns are partly predictable.

After reviewing some of the basic preprocessins techniques for handling safety stocks and multilevel problems, we discusss a variety of aspects arising particularly in small and large bucket (time period) models such as stars-ups, changeovers, minimum batch sizes, choice of one or two set-ups...

We describe in this paper a method allowing to order submodels in linear regression. A real function is attached to each submodel, allowing to graphically compare and order them. Our procedure defines an objective function depending on two factors (lack of fit and multicolinearity) with the...

We show that the empirical process of the squared residuals of an ARCH(p) sequence converges in distribution to a Gaussian process B (F(t)) + tf(t)E , where F is the distribution function of the squared innovations, f its derivative, {B(t), 0= t = 1} a Brownian bridge and E a normal random...