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Forecasting the population of Poland is very challenging. Firstly, the country has been undergoing rapid demographic changes. In the 1990s, Poland experienced a fundamental shift from a communist regime to a democratic regime and entered the European Union in 2004. The political, economic, and social changes that accompanied the transformation had...
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... Eurostat claims that there will be a continuous drop in the TFR to 1.18 in the years 2006-2009 followed by a rise to 1.64 in 2034 with no change afterwards, the UN assumes a linear increase to nearly 1.8 in 2050 from the beginning of the forecast period. Nevertheless, although our point forecast differs from those of the CSO, UN, and Eurostat, our 80% prediction intervals cover the whole range of predicted values and even allow for the possibility that fertility will fall further (Figure 2). ...
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Forecasting the population of Poland is very challenging. Firstly, the country has been undergoing rapid demographic changes. In the 1990s, they were influenced by the political, economic, and social consequences of the collapse of the communist regime. Since 2004 they have been shaped by Poland’s entry into the European Union. Secondly, the availa...
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... They used a principal component method to extract a single timevarying index of the level of mortality rates, from which the forecasts are obtained using a random walk with drift. Since then, this method has been widely used for forecasting mortality rates in various countries, including Australia (Booth, Maindonald, and Smith 2002; De Jong and Tickle 2006), Austria (Carter and Prskawetz 2001), Belgium (Brouhns, Denuit, and Vermunt 2002), Canada (Lee and Nault 1993), Chile (Lee and Rofman 1994), China (Lin 1995), England & Wales (Cairns et al. 2011), Finland (Alho 1998), Japan (Wilmoth 1996), Norway (Keilman, Pham, and Hetland 2002), Spain (Felipe, Guillén, and Marín 2002; Debón, Montes, and Sala 2006), Sweden (Lundström and Qvist 2004; Tuljapurkar 2005), the U.K. (Renshaw and Haberman 2003a), the Nordic countries (Koissi, Shapiro, and Högnäs 2006 ), the seven most economically developed nations (G- 7) (Tuljapurkar, Li, and Boe 2000), and United State (Cairns et al. 2011). The strengths of the Lee-Carter (LC) method are its simplicity and robustness in situations where age-specific log mortality rates have linear trends (Booth et al. 2006). ...
Using the age- and sex-specific data of 14 developed countries, we compare the point and interval forecast accuracy and bias of ten principal component methods for forecasting mortality rates and life expectancy. The ten methods are variants and extensions of the Lee-Carter method. Based on one-step forecast errors, the weighted Hyndman-Ullah method provides the most accurate point forecasts of mortality rates and the Lee-Miller method is the least biased. For the accuracy and bias of life expectancy, the weighted Hyndman-Ullah method performs the best for female mortality and the Lee-Miller method for male mortality. While all methods underestimate variability in mortality rates, the more complex Hyndman-Ullah methods are more accurate than the simpler methods. The weighted Hyndman-Ullah method provides the most accurate interval forecasts for mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy for life expectancy.
... Problem prognoziranja migracionih tokova U poređenju sa fertilitetom i mortalitetom, migracije su komponenta koja je generalno najteža za prognoziranje (Matysiak, Nowok, 2006), naročito u državama poput Srbije. Postoji više razloga za to, i oni se mogu svrstati u dve osnovne grupe. ...
... Međutim, odluka o načinu formiranja migracionog varijabiliteta tokom projekcionog perioda morala se svesti na čisto subjektivnu ocenu autora. Iskustva drugih autora probabilističkih projekcija potvrđuju visok stepen subjektivne ocene zbog problema sa nedovoljno pouzdanim podacima, nedovoljno dugim vremenskim serijama, odnosno opštim problemom slabe predvidivosti ove komponente (de Beer, Alders, 1999;Alho, 2001;Matysiak, Nowok, 2006;Torri, Vignoli, 2007). ...
Uvod Jedna od glavnih implikacija današnje nepovoljne starosne strukture stanovništva Srbije 1 ogleda se u ekonomskom pritisku koji radni kontingent trpi od strane brzorastuće populacije starih. Porastu pritiska dodatno doprinosi smanjenje radne populacije, naročito njenog mla dela (stari 20-44 godine), koji je ujedno i glavni nosilac reproduktivne funkcije stanovništva. Odnos izme broja starih osoba i veličine radnog kontingenta, izražen koeficijentom zavisnosti starih, 2 je od vitalnog značaja za stabilnost ekonomskog sistema države. U tom pogledu, Srbija je pri vrhu liste zemalja koje odlikuju nepovoljne vrednosti koeficijenta, ali ne i u vrhu samo zahvaljujući činjenici da dužina prosečnog životnog veka nije na nivou najrazvijenijih država sveta. 3 Aktuelna starosna piramida stanovništva Srbije (grafikon 1) ukazuje da će već u narednih deceniju-dve doći do kritičnog disbalansa u odnosu veličine radne populacije i populacije starih. Tu se postavlja suštinsko pitanje na koje ukazuje ovaj rad. Kako obezbediti radnu snagu neophodnu da izdrži opterećenje koje će brzorastuća populacija starih nametnuti već u bliskoj budućnosti? Porast nivoa plodnosti je dugoročno neophodan uslov za stabilizaciju starosne piramide Srbije, ali ostvarenje tog cilja krije dva kratkoročna problema. Jedan je neizbežno odloženo dejstvo porasta fertiliteta na popunu radne populacije, čak i da do promene do momentalno, jer se pozitivni efekti takve promene mogu osetiti tek nakon više od dve decenije. No, tokom tog perioda, pritisak na permanentno smanjujući radni kontingent bio bi kontinuirano pojačavan usled uvećanja populacije mladih. Drugim rečima, * Centar za demografska istraživanja Instituta društvenih nauka, Beograd.
Intense industrialization of Serbia during the period of socialist Yugoslavia induced voluminous internal migration, mainly from villages to the fast-growing industrial centers, which resulted in a disturbed sex composition of the current population in the prime reproductive ages (20–39 years) at the settlement level of the country. As a result, both agrarian zones of young men surplus and urban “oases” of young women surplus jointly reinforce the processes of demographic aging and poverty in Serbia, despite the goals of policy makers presented through crucial national strategies regarding sustainable development of the country. The rural zones with a deficit of young women, which are predominantly border and mountain regions, are the first to experience the negative effects of the prevailing demographic trend in the future. Some of the findings in this chapter point to the typical positive feedback loop “population–poverty” as the intrinsic mechanism of persistent “highlands to lowlands” migration. Finally, the probabilistic population projection of mountain regions in Serbia indicates decreasing and aging of its population as an inevitable and dominant demographic process in the next few decades. These tendencies could be substantial obstacles to efforts in achieving sustainable development of Serbia’s mountain regions.
Based on the example of the forecast of the population of Central Serbia for the 2005-2032 period, the basic probabilistic concept in forecasting the trends of demographic components of population development have been presented. The stochastic element of forecast is based on the analysis of empirical forecast errors of corresponding indicators of demographic development. The analysis frameworks were forecasts of official bureaus of statistics published during the second half of the 20th century. The statistical distribution of probability for chosen forecast parameters were formed around so called middle variant of corresponding indicators in the current national forecast of population, published by the Statistical Office of the Republic of Serbia (SORS) for the period 2002-2032. The basic characteristics of the probabilistic approach of forecasting were presented through mutual comparison of the main methodological assumptions, namely though comparative demographic-statistical valorization of results with traditional deterministic concept, represented by forecasts of RSB. The stochastic forecast of the population of Serbia clearly indicated to the key advantages of this approach: methodological consistency in quantifying demographic indicators as well as the possibility of transparent usage of results in numerous aspects of social planning. In this way the significance of the necessity for the elaboration of a studious national forecast of the population of Serbia completely based on a stochastic basis has been stressed, regardless of the still-present restrictions in the development of the probabilistic approach.
"Multi-model ensemble climate projections in the ENSEMBLES Project of the EU allowed the authors to quantify selected extreme-weather indices for Poland, of importance to climate impacts on systems and sectors. Among indices were: number of days in a year with high value of the heat index; with high maximum and minimum temperatures; length of vegetation period; and number of consecutive dry days. Agricultural, hydrological, and human health indices were applied to evaluate the changing risk of weather extremes in Poland in three sectors. To achieve this, model-based simulations were compared for two time horizons, a century apart, i.e., 1961–1990 and 2061–2090. Climate changes, and in particular increases in temperature and changes in rainfall, have strong impacts on agriculture via weather extremes – droughts and heat waves. The crop yield depends particularly on water availability in the plant development phase. To estimate the changes in present and future yield of two crops important for Polish agriculture i.e., potatoes and wheat, some simple empirical models were used. For these crops, decrease of yield is projected for most of the country, with national means of yield change being: −2.175 t/ha for potatoes and –0.539 t/ha for wheat. Already now, in most of Poland, evapotranspiration exceeds precipitation during summer, hence the water storage (in surface water bodies, soil and ground) decreases."
The aim of the 'Uncertain Population of Europe'(UPE) project was to compute long-term stochastic (probabilistic) population forecasts for 18 European countries. We developed a general methodology for constructing predictive distributions for fertility, mortality and migration. The assumptions underlying stochastic population forecasts can be assessed by analysing errors in past forecasts or model-based estimates of forecast errors, or by expert judgement. All three approaches have been used in the project. This article summarizes and discusses the results of the three approaches. It demonstrates how the-sometimes conflicting-results can be synthesized into a consistent set of assumptions about the expected levels and the uncertainty of total fertility rate, life expectancy at birth of men and women, and net migration for 18 European countries.
Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.
Interdisciplinary studies that draw on long-term, global population projections often make limited use of projection results, due at least in part to the historically opaque nature of the projection process. We present a guide to such projections aimed at researchers and educators who would benefit from putting them to greater use. Drawing on new practices and new thinking on uncertainty, methodology, and the likely future courses of fertility and life expectancy, we discuss who makes projections and how, and the key assumptions upon which they are based. We also compare methodology and recent results from prominent institutions and provide a guide to other sources of demographic information, pointers to projection results, and an entry point to key literature in the field.
