Continuing increases in life expectancy beyond previously-held limits have brought to the fore the critical importance of mortality forecasting. Significant developments in mortality forecasting since 1980 are reviewed under three broad approaches: expectation, extrapolation and explanation. Expectation is not generally a good basis for mortality forecasting, as it is subjective; expert expectations are invariably conservative. Explanation is restricted to certain causes of death with known determinants. Decomposition by cause of death poses problems associated with the lack of independence among causes and data difficulties. Most developments have been in extrapolative forecasting, and make use of statistical methods rather than models developed primarily for age-specific graduation. Methods using two-factor models (age-period or age-cohort) have been most successful. The two-factor Lee–Carter method, and, in particular, its variants, have been successful in terms of accuracy, while recent advances have improved the estimation of forecast uncertainty. Regression-based (GLM) methods have been less successful, due to nonlinearities in time. Three-factor methods are more recent; the Lee–Carter age-period-cohort model appears promising. Specialised software has been developed and made available. Research needs include further comparative evaluations of methods in terms of the accuracy of the point forecast and its uncertainty, encompassing a wide range of mortality situations.

We compare the short-to medium-term accuracy of five variants or extensions of the Lee-Carter method for mortality forecasting. These include the original Lee-Carter, the Lee-Miller and Booth-Maindonald-Smith variants, and the more flexible Hyndman-Ullah and De Jong-Tickle extensions. These methods are compared by applying them to sexspecific populations of 10 developed countries using data for 1986-2000 for evaluation. All variants and extensions are more accurate than the original Lee-Carter method for forecasting log death rates, by up to 61%. However, accuracy in log death rates does not necessarily translate into accuracy in life expectancy. There are no significant differences among the five methods in forecast accuracy for life expectancy.

Creating the conditions that foster student engagement, success and retention remains a perennial issue within the higher education sector. Traditionally satisfaction has been prioritised in assessing student success. A more expansive, holistic and ontological perspective of the student experience that takes into account who and what students are becoming is required. This study develops a holistic approach to measuring student engagement. It models and measures two antecedents to engagement, namely involvement and expectations, four dimensions of engagement, namely affective, social, cognitive and behavioural engagement, and their relative and differential impact upon five specific student and institutional success outcomes namely, institutional reputation, student wellbeing, transformative learning, selfefficacy and self-esteem. A survey with a sample of 952 tertiary students enrolled at a major Australian tertiary institution was employed. A structural model was then specified to assess the structural relationships between the constructs. The results show that student expectations and involvement have an important seeding role in student engagement. Affective engagement was the most important determinant of institutional reputation, wellbeing, and transformative learning. Behavioural engagement determined self-efficacy and self-esteem. Cognitive and social engagement were necessary but not sufficient conditions for student success.

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Objectives: To use new methodology to forecast mortality for use in projections of the elderly population of Australia and to compare them with official projections. Method: The Lee‐Carter method is applied to data for Australian females and males for 1968–2000 to forecast mortality to 2031. These forecasts are used with standard population projection methods to produce projections of the elderly population. Results: By 2027, forecast life expectancy is 88.1 and 82.9 years for females and males, compared with official projections of 85.4 and 81.4 years. Over the period to 2031, the populations aged 65+ and 85+ are forecast to increase by factors of 2.3 and 3.4 respectively. Compared with official projectrons, the forecast elderly population is substantially larger and has higher old‐age dependency ratios, higher proportions aged 85+ and lower sex ratios. Conclusion: Official projections underestimate the size of the future elderly population especially the female and ola'est‐old populations.