We introduce a generalization of the approximate factor model that divides the observable variables into groups, allows for arbitrarily strong cross-correlation between the disturbance terms of variables that belong to the same group, and for weak correlation between the disturbances of...

The aim here is to show how to obtain many of the well-known limit results (i.e., central limit theorem, law of the iterated logarithm, invariance principle) of stochastic approximation (SA) by a shorter argument and under weaker conditions. The idea is to introduce an artificial sequence,...

In this work we derive the usual limit laws (weak and strong convergence, central limit theorem, invariance principle) for stochastic approximation with stationary noise. The idea is to introduce an artificial sequence, related to the SA scheme, but which clearly obeys the desired limit law....

A number of algorithms are presented for calculating the exact likelihood of a multivariate ARMA model. There are two aspects to the algorithms. Firstly, the parameterization is in terms of AR parameters and autocovariances. This obviates difficulties with initial MA estimates. Secondly, the...

Most studies of the returns to higher education ignore the effects of taxation on the distribution of the costs and benifits of education. In an analysis of hiogher education in Fiji, this study incorporates the tax sysyem in a model of the returns to education and finds that, rather than...