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In this work, we have performed a simulation to study the structural characteristics and phase transformation in Al2O3 glass under compression. The structural characteristics of Al2O3 glass were examined via AlOx units, OAly linkages, the average bond distance distributions, order parameters, and visualization of simulation data. The result showed that the network structure of Al2O3 glass is built mainly by AlOx (x = 3, 4, 5, 6, 7) units that are linked to each other via common O atoms. We found that the distribution of AlOx units in network structure is not uniform but tends to form clusters contained AlOx units. In addition, during a moderately long time, the glass has a two-phase that consists of separate low-density (LD) and high-density (HD) phases. The size of these phases significantly depends on the compression.

The labour force surveys (LFSs) on all Eurostat countries underwent a substantial redesign in January 2021. To ensure coherent labour market time series for the main indicators in the Norwegian LFS, we model the impact of the redesign. We use a state-space model that takes explicit account of the rotating pattern of the LFS. We also include auxiliary variables related to employment and unemployment that are highly correlated with the LFS variables we consider. The results of a parallel run are also included in the model. This paper makes two contributions to the literature on the effects of LFS redesign. First, we suggest a symmetric specification of the process of the wave-specific effects. Second, we account for substantial fluctuations in the labour force estimates during the Covid-19 pandemic by applying time-varying hyperparameters. Likelihood-ratio tests and examination of the auxiliary residuals show the latter to be warranted.

X-12-ARIMA har vært brukt i Statistisk sentralbyrå i mange år som et verktøy for sesongjustering. Det er en ikke parametrisk metode, der trend- og sesongkomponent blir estimert ved glidende gjennomsnittsteknikk, ikke modeller. Vi får dermed estimerte verdier for trend-, sesongkomponent og sesongjusterte tall uten usikkerheter. Pfeffermann har laget en metode for å anslå disse usikkerhetene. Vi anvender denne metoden for å beregne usikkerheter av trend og sesongjusterte tall for prosent arbeidsledige totalt. Vi beregner også usikkerhetene for 3-måneders glidende gjennomsnitt og 3-månedersendring av sesongjusterte tall. Variansestimeringen tar hensyn til korrelasjonen til utvalgsfeil som kommer fra en roterende panelundersøkelse.

Vi lever stadig lenger. Her presenteres en analyse av levealderen og aldersmønsteret i dødeligheten for norske menn og kvinner siden 1900. Vi har brukt den såkalte Lee-Cartermodellen for å kunne gi anslag på dødelighetsutviklingen i framtiden. Vi forventer en fortsatt økning i levealderen, helt opp til 86 år for menn og 90 år for kvinner i 2060.

We analyse empirical errors observed in historical population forecasts produced by statistical agencies in 14 European countries since 1950. The focus is on forecasts for three demographic variables: fertility (Total Fertility Rate - TFR), mortality (life expectancy at birth), and migration (net migration). We inspect forecast bias and forecast accuracy in the historical forecasts, as well as the distribution of the errors. Finally, we analyse for each of the three variables correlation patterns in forecast errors across countries and, for mortality, the correlation between errors for men and women. In the second part of the report we use time series model to construct prediction intervals to 2050 for the TFR, the life expectancy for men and women, and net migration in 18 European countries. GARCH models are used for fertility and mortality, while net migration is modelled as an autoregressive process Keywords: stochastic population forecast, empirical forecast errors, prediction intervals, GARCHmodels, TFR, life expectancy, net migration, EEA

When using material from this publication, please give Statistics Norway as your source. The demographic future of any population is uncertain, but some of the many possible trajectories are more probable than others. Therefore, an exploration of the demographic future should include two elements: a range of possible outcomes, and a probability attached to that range. Together, these two constitute a prediction interval for the population variable concerned. This report presents the findings of a research project, the aim of which was to compute prediction intervals for the future population of Norway broken down by age and sex to the horizon 2050. We estimate that the odds are four against one (80 per cent chance) that Norway’s population, now 4.5 million, will number between 4.3 and 5.4 million in the year 2025, and 3.7-6.4 million in 2050. This illustrates that uncertainty increases with time. There is a clear trade-off between greater accuracy (higher odds) and higher precision (narrower intervals). Odds of 19 against one (95 per cent chance) result in a wider interval: 4.1-5.7 million in 2025, and 3.2-7.3 million in 2050. The probabilistic population forecasts of the youngest and the oldest age groups show largest uncertainty, because fertility and mortality are hard to predict. As a result, prediction intervals in 2030 for the population younger than 20 years are so wide, that the forecast is not very informative. International migration shows large prediction intervals around expected levels, but its impact on the age structure is modest. In 2050, uncertainty has cumulated so strongly, that intervals are very large for virtually all age groups, in particular when the intervals are judged in a relative sense (compared to the median forecast). According to our statistical model, the expected accuracy of the total population size forecast published by Statistics Norway is somewhat below two-thirds on the long run, and a little above that level on the short run. The results have been obtained on the basis of stochastic simulation of each of the three components of population change; fertility, mortality, and international migration. Simulation of the components relied heavily on three complementary methods: time series analysis for the historical development of key demographic indicators, such as the TFR, the life expectancy, and numbers of immigrants and emigrants;an analysis of historical forecast errors, assembled on the basis of forecasts produced by Statistics Norway since 1969;and finally expert judgement, which was used, for instance, to restrict the prediction interval for the TFR or that for the numbers of immigrants and emigrants to a reasonable range. The predictions for each component were calibrated in such a way that the median coincided with the Medium Variant value of the 1999-based official population forecast of Statistics Norway. The time series predictions indicated that assumptions on future TFR as employed by Statistics Norway in its official population forecasts have estimated coverage probabilities of only 46, 31, and 24 per cent in the years 2010, 2030, and 2050. The official mortality (i.e. life expectancy) assumptions have higher expected accuracy in 2050 (just over 60 per cent), but lower accuracy in the beginning of this century (just over a third in the period 2000-2010).