Exploratory factor analysis (EFA) is an important tool in data analyses, particularly in social science. Usually four steps are carried out which contain an large number of options. One important option is the number of factors and the association of variables with a factor. Our tools aim to...

This paper identifies new classifications of Australian universities based on their total and per-academic staff research outputs using the data for the period 1998-2002. We define research performance in terms of audited numbers of PhD completions, publications and grants (in accordance with...

Exploratory factor analysis (EFA) is an important tool in data analyses, particularly in social science. Usually four steps are carried out which contain a large number of options. One important option is the number of factors and the association of variables with a factor. Our tools aim to...

There is a growing policy focus in Australian higher education on quantitative research performance assessment. However, most of the analysis has addressed aggregate performance at the institutional level, an approach inconsistent with recent policy emphasis on diversity among universities, and...

In this paper, we introduce a methodology that allows for imposing views on density forecasts of a (frequency domain) factor based time series model. Such a model produces a density forecast for the future evolution of economic and financial variables such as interest rates, asset returns and...

Independent component analysis (ICA) is a modern factor analysis tool de- veloped in the last two decades. Given p-dimensional data, we search for that linear combination of data which creates (almost) independent components. Here copulae are used to model the p-dimensional data and then...

In this paper we analyze spot prices and futures quotation data to get inference under both the historical and the risk neutral measure in a commodity crude oil market (data are referred to WTI index which tracks the crude oil barrel price on NYMEX market).While big part of research and...

We analyze the Large Deviation Probability (LDP) of linear factor models generated from non-identically distributed components with regularly-varying tails, a large subclass of heavy tailed distributions. An efficient sampling method for LDP estimation of this class is introduced and...

We propose a new approach to sample unobserved states conditional on available data in (conditionally) linear unobserved component models when some of the observations are missing. The approach is based on the precision matrix of the states and model variables, which is sparse and banded in many...

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...