A systematic review protocol on the use of teaching portfolios for educators in further and higher education.

portfolio and undergraduate students have suggested that a teachingportfolio may have a benefit for educators in higher education as a means to providerelevancy and focus to their teaching.Design. The objectives of the review are to evaluate how a teaching portfolio assistseducators in teaching and learning; to evaluate the effects of maintaining a teachingportfolio for educators in relation to personal development; to explore the type ofportfolio used; to determine whether a teaching portfolio is perceived more beneficialfor various grades and professional types; and to determine any motivatingfactors or workplace incentives behind its implementation and completion. A searchof the following databases will be made MEDLINE, CINAHL, BREI, ERIC andAUEI. The review will follow the Joanna Briggs Institute guidance for systematicreviews of quantitative and qualitative research.Conclusion. The review will offer clarity and direction on the use of teachingportfolios, for educators, policymakers, supervisory managers and researchers involvedin further and higher education.

Incorporating Higher Moments into Value at Risk Forecasting

Value-at-risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a simple approach to forecasting of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting the standard normal distribution with the first four moments, which are allowed to vary over time. In an extensive empirical study, we compare the GCE approach to other models of VaR forecasting and conclude that it provides accurate and robust estimates of the realized VaR. In spite of its simplicity, on our dataset GCE outperforms other estimates that are generated by both constant and time-varying higher-moments models.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) speci?cation with binomial thinning and Poisson innovations, we examine both the asymptotic e?ciency and ?nite sample properties of the ML estimator in relation to the widely used conditional least
squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justi?ed, there are substantial gains to be had from using ML especially when the thinning parameters are large.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

Corrigendum Vol. 30, Issue 2, 259, Article first published online: 15 MAR 2009 to correct the order of authors names: Bu R., K. Hadri, and B. McCabe.