Pension Model
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Pension Model
For pensions, a reliable Pension model is necessary for system simulations and projections, so it is important to have a sound database for pension system analyses. For an example of a complex pension model see e.g. (Deloitte, 2011). A pension system and its financing are some of the most important but also some of the most difficult roles of a modern country. Every prosperous community has a pension system at present; the citizens rely on its stability and the system provides most of them with the main part of their income in old age. The system's stability and financial sustainability are some of the key preconditions for successful operation of the state and satisfaction of its citizens. Taxonomy of the Pension Models The summary of the pension system taxonomy is based on a study by (Gál, Horváth, Orbán, & Dekkers, 2009), see also (Deloitte, 2011). The chart below provides an overview of the basic types of models used in various EU countries for pension system modelling. ...
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Pension
A pension (, from Latin ''pensiō'', "payment") is a fund into which a sum of money is added during an employee's employment years and from which payments are drawn to support the person's retirement from work in the form of periodic payments. A pension may be a "defined benefit plan", where a fixed sum is paid regularly to a person, or a "defined contribution plan", under which a fixed sum is invested that then becomes available at retirement age. Pensions should not be confused with severance pay; the former is usually paid in regular amounts for life after retirement, while the latter is typically paid as a fixed amount after involuntary termination of employment before retirement. The terms "retirement plan" and "superannuation" tend to refer to a pension granted upon retirement of the individual. Retirement plans may be set up by employers, insurance companies, the government, or other institutions such as employer associations or trade unions. Called ''retirement plans' ...
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Cohort (statistics)
In statistics, marketing and demography, a cohort is a group of subjects who share a defining characteristic (typically subjects who experienced a common event in a selected time period, such as birth or graduation). Cohort data can oftentimes be more advantageous to demographers than period data. Because cohort data is honed to a specific time period, it is usually more accurate. It is more accurate because it can be tuned to retrieve custom data for a specific study. In addition, cohort data is not affected by tempo effects, unlike period data. On the contrary, cohort data can be disadvantageous in the sense that it can take a long amount of time to collect the data necessary for the cohort study. Another disadvantage of cohort studies is that it can be extremely costly to carry out, since the study will go on for a long period of time, demographers often require sufficient funds to fuel the study. Demography often contrasts cohort perspectives and period perspectives. Fo ...
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Cross-sectional Data
Cross-sectional data, or a cross section of a study population, in statistics and econometrics, is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at the one point or period of time. The analysis might also have no regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects. For example, if we want to measure current obesity levels in a population, we could draw a sample of 1,000 people randomly from that population (also known as a cross section of that population), measure their weight and height, and calculate what percentage of that sample is categorized as obese. This cross-sectional sample provides us with a snapshot of that population, at that one point in time. Note that we do not know based on one cross-sectional sample if obesity is increasing or decreasing; we can only describe the current proportion. Cross-sectional data differs from time ...
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Panel Data
In statistics and econometrics, panel data and longitudinal data are both multi-dimensional data set, data involving measurements over time. Panel data is a subset of longitudinal data where observations are for the same subjects each time. Time series and cross-sectional data can be thought of as special cases of panel data that are in one dimension only (one panel member or individual for the former, one time point for the latter). A study that uses panel data is called a longitudinal study or panel study. Example In the multiple response permutation procedure (MRPP) example above, two datasets with a panel structure are shown and the objective is to test whether there's a significant difference between people in the sample data. Individual characteristics (income, age, sex) are collected for different persons and different years. In the first dataset, two persons (1, 2) are observed every year for three years (2016, 2017, 2018). In the second dataset, three persons (1, 2, 3) ...
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Population Ageing
Population ageing is an increasing median age in a population because of declining fertility rates and rising life expectancy. Most countries have rising life expectancy and an ageing population, trends that emerged first in developed countries but are now seen in virtually all developing countries. That is the case for every country in the world except the 18 countries designated as "demographic outliers" by the United Nations. The aged population is currently at its highest level in human history.World Population Ageing: 1950-2050
United Nations Population Division.
The UN predicts the rate of population ageing in the 21st century will exceed that of the previous century. The number of people aged 60 years and over has tripled since 1950 and reached 600 million ...
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Economic Growth
Economic growth can be defined as the increase or improvement in the inflation-adjusted market value of the goods and services produced by an economy in a financial year. Statisticians conventionally measure such growth as the percent rate of increase in the real gross domestic product, or real GDP. Growth is usually calculated in real terms – i.e., inflation-adjusted terms – to eliminate the distorting effect of inflation on the prices of goods produced. Measurement of economic growth uses national income accounting. Since economic growth is measured as the annual percent change of gross domestic product (GDP), it has all the advantages and drawbacks of that measure. The economic growth-rates of countries are commonly compared using the ratio of the GDP to population (per-capita income). The "rate of economic growth" refers to the geometric annual rate of growth in GDP between the first and the last year over a period of time. This growth rate represents the trend in ...
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Dynamic Microsimulation Pension Model
A dynamic microsimulation pension model is a type of a pension model projecting a pension system by means of a microsimulation and generating the complete history of each individual in a data set. The results of such model offer both the aggregate (e.g. total replacement ratio, implicit debt) and individual indicators (e.g. individual cash-flows) of the pension system. Thanks to complexity of results, there is a possibility to investigate the distribution of pensions, poverty of pensioners, impact of the changes of the pension formula, for more examples see e.g. (Deloitte, 2011). Detailed individual set of (administrative) data should serve as a model input. Dynamic Microsimulation Pension Models A dynamic microsimulation pension models (or a dynamic model with dynamic ageing) is a type of a pension model – see its pension model#Taxonomy of the Pension Models, taxonomy and also (Gál, Horváth, Orbán, & Dekkers, 2009) . There are two basic types of this kind of model - (i) de ...
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Monte Carlo Simulation
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution. In physics-related problems, Monte Carlo methods are useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures (see cellular Potts model, interacting particle systems, McKean–Vlasov processes, kinetic models of gases). Other examples include modeling phenomena with significant uncertainty in inputs such as the calculation of risk in b ...
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