Advances in population projections

In this paper, we develop a generalised GARCH-based stochastic mortality model with a view to incorporate conditional heteroskedasticity and conditional non-normality in stochastic mortality modelling. We provide an empirical analysis of the UK mortality rates from 1922 to 2009 and find that both features are long-term behaviour of mortality structures. These structures impact the valuation and hedging of longevity-related insurance products and have been largely overlooked in many existing literature except for a very recent work by Giacometti et. al (2012), where only conditional heteroskedasticity is considered. To describe conditional non-normality, we adopt a Double Exponential distribution, also capable of incorporating the conditional skewness and leptokurtosis in our dataset. For the practical implementation, as in Siu, Tong and Yang (2004), we propose a user-friendly two-stage estimation scheme. At the first stage, we employ the Quasi-Maximum Likelihood Estimation (QMLE) to estimate the GARCH structure whilst at the second stage we adopt the MLE to estimate the Double Exponential parameters using residuals as inputs. We also examine the forecasting performance of the proposed model and find that the Double Exponential GARCH model provides reasonably good forecasts for future mortality developments.

Countries and demographers differ regarding the definition, estimation, and projection of international migrant stocks and migration flows. Despite these difficulties, the United Nations Population Division prepares, every two years, estimates, referring to past quantities, and projections, referring to future quantities, of net migration (immigration minus emigration) for all countries and regions of the world. We compare two examples taken from the World Population Prospects: The 2010 Revision (United Nations, Department of Economic and Social Affairs, Population Division, 2011), with alternative projections of net migration based on gravity-type models for migration flows (Cohen et al., 2008 and Kim and Cohen, 2010). The two examples refer to (a) net migration from less developed regions to more developed regions and (b) net migration from the rest of the world, excluding the United States of America, to the United States of America. In both examples, the UN projections assume net migration declines to 0 by 2100. By contrast, in the gravity-model projections, net migration rises smoothly to a plateau about twice as high as present estimates of net migration; net migration ceases to increase as the populations of origin and destination countries stabilize.

Population change affects national income, national expenditure, and the demand for services such as education, health and transport. Therefore, information about future population size and structure obtained with the help of population forecasts, which can be used for a wide range of decision-making purposes, is of paramount importance. The primary aim of this paper is to produce three different types of population forecasts for Kazakhstan till 2030 and by comparing and analysing the differences to find out the most important factors determining the population development process in the country. Kazakhstan is a country with significant size and regional diversity which makes it relevant to consider those dimensions in population forecasting. Most southern oblasts of the country have a young population structure meaning that much of future population growth, particularly of working age, will come from these regions. Also, native population tends to concentrate in rural areas, while industrialized cities are mostly populated by non-natives with considerably different nuptiality and fertility behaviour. Despite such regional and residential demographic differences, presently the country is experiencing an overall increase in birth rates. The question is, how long will last this favorable fertility situation.

In an ex post facto test of a modification of the Root Mean Square Error approach to developing 66% confidence intervals for population forecasts made using the Hamilton-Perry Method, we find that in a sample of nine states (one from each of the nine census division), that the intervals contain the 2010 population in 126 of 162 observations (77.8%) and similar results for an ex post facto test against 2000 data. We find the results encouraging. The paper describes the Root Mean Square Error approach as well as the data and the Hamilton-Perry Method.