Lithuanian Journal of Statistics
ISSN / EISSN : 1392-642X / 2029-7262
Published by: Vilnius University Press (10.15388)
Total articles ≅ 135
Latest articles in this journal
Lithuanian Journal of Statistics, Volume 60, pp 44–58-44–58; https://doi.org/10.15388/ljs.2021.26447
The Winter Olympics have been held since 1924, and each time host countries spend billions on organization, so it is important for them to know if this pays off in the future. This paper examines whether hosting the Winter Olympic Games yields long-term economic benefits. To achieve this, the difference-in-difference model for relative changes in Gross Domestic Product per capita was estimated. A difference-in-difference estimator examines post-Olympic impacts for host countries between 1972 and 2014. Regression results provide no additional long term impacts of hosting the Winter Olympics on GDP per capita.
Lithuanian Journal of Statistics, Volume 60, pp 29–40-29–40; https://doi.org/10.15388/ljs.2021.26445
Academic institutions are seeking to attract the most capable and prospective students. Many research studies seek to identify factors determining a successful transition from secondary to tertiary education. One of the important issues is the predictive value of national testing/Matura examination results in relation to higher education institutions admission. The aim of our study is to quantify the odds to study at university and the results of centralised student assessments (the 10th grade and Matura) of mathematics and the Lithuanian language and literature for the period of five years (Matura examinations for the period 2014–2018), taking into account the student's gender, the location of the school attended, social support, and special needs indicators. We estimate that the Matura grade in mathematics is almost double more important when compared to the10th grade test results for odds of studying at higher education institutions. Grades of the Lithuanian language and literature obtained to be twice more import for males than females. From 2023 or 2024, we may observe the downward trend of people with tertiary educational attainment in Lithuania.
Lithuanian Journal of Statistics, Volume 60, pp 1–7-1–7; https://doi.org/10.15388/ljs.2021.26442
Bayes multiclass classification of spatial Gaussian data following the universal kriging model is considered. The closed-form expressions for the maximum likelihood (ML) estimator of regression parameters and the actual error rate (AER) in terms of semivariograms are derived.
Lithuanian Journal of Statistics, Volume 60, pp 41–43-41–43; https://doi.org/10.15388/ljs.2021.26446
A short message is dedicated to professor Gunnar Kulldorff who was the founder of the Baltic-Nordic-Ukrainian Network on Survey Statistics, and the President of the International Statistical Institute.
Lithuanian Journal of Statistics, Volume 60, pp 8–21-8–21; https://doi.org/10.15388/ljs.2021.26443
The purpose of this article is to present a methodology and results for nowcasting poverty and inequality indicators during economic growth and the Covid-19 pandemic in Lithuania. Nowcasting combines the techniques of tax-benefit microsimulation and calibration of the survey weights. For the microsimulation, the tax-benefit microsimulation model EUROMOD is used together with its additional components for Lithuania, which were developed by the Ministry of Social Security and Labour of the Republic of Lithuania. Three economic forecasts, developed by the Bank of Lithuania for 2020, are used: the rapid V-shaped recovery scenario, intermediate U-shaped recovery scenario and a slow extended U-shaped recovery scenario. The results show Lithuania's favourable tendencies in reducing poverty and inequality in the general population and by age groups in the context of rapid economic growth and improving the improved labour-market situation in 2018–2019. The results of 2020 suggest that relative at-risk-of-poverty rates and inequality in the country are likely to decline. The foreseen decrease in the at-risk-of-poverty rate is primarily due to reducing poverty risk among older people and children. The most vulnerable age groups include youth (18–24 years) and the elder working-age population (50–64 years). Poverty rates for these groups are likely to increase in 2020. However, it should be noted that the at-risk-of-poverty rates had also declined in Lithuania during the first years of the previous economic crisis. Decomposition of demographic/employment changes and policy effects for 2019–2020 show that due to demographic and employment changes, poverty and inequality is likely to increase in Lithuania in 2020. The impact of the policy effect is progressive, more favourable to the less well-off, leading to a reduction in poverty. Progressiveness is due to the fact that during the quarantine period, flat benefits were provided to a large part of the society: children, pensioners, job-seekers, self-employed.
Lithuanian Journal of Statistics, Volume 59, pp 1-13; https://doi.org/10.15388/ljs.2020.16784
Straipsnyje atliekamas Lietuvos Respublikos Vyriausybės 2014–2020 metų užimtumo didinimo programoje pateiktų pagrindinių darbo rinkos rodiklių vertinimas. Remiantis šiais rodikliais analizuojama situacija darbo rinkoje bei tendencijos šalyje, stebimi nukrypimai nuo prognozuotų rodiklių (numatytų užimtumo programos įgyvendinimo siekių) reikšmių, įvertinamos jų tobulinimo galimybės. Nepaisant bendrųjų situacijos darbo rinkoje rodiklių analizės svarbos, vienas iš esminių šios publikacijos prioritetų – užimtumo programoje išskirtų tikslinių grupių integracijos galimybių įvertinimas, kurios išreiškiamos remiantis darbo rinkos politikos rezultatus atspindinčiais rodikliais. Atsižvelgiant į užimtumo programoje pateiktų darbo rinkos rodiklių sistemą, kurie daugiausia orientuoti į darbo vietų kaitą (kūrimą), prioritetas teikiamas užimtumo rodikliams. Be tiesiogiai darbo rinkos politikos rezultatus atspindinčių statistinių duomenų, straipsnyje analizuojami ir papildomi tikslinių grupių situaciją darbo rinkoje atspindintys rodikliai.
Lithuanian Journal of Statistics, Volume 58, pp 39-47; https://doi.org/10.15388/ljs.2019.16669
We investigate linear and nonlinear hypotheses testing in a Cox proportional hazards model for right-censored survival data when the covariates are subject to measurement errors. In Kukush and Chernova (2018) [Theor. Probability and Math. Statist. 96, 101–110], a consistent simultaneous estimator is introduced for the baseline hazard rate and the vector of regression parameters. Therein the baseline hazard rate belongs to an unbounded set of nonnegative Lipschitz functions, with fixed constant, and the vector of regression parameters belongs to a compact parameter set. Based on the estimator, we develop two procedures to test nonlinear and linear hypotheses about the vector of regression parameters: Wald-type and score-type tests. The latter is based on an unbiased estimating equation. The consistency of the tests is shown.
Lithuanian Journal of Statistics, Volume 58, pp 4-15; https://doi.org/10.15388/ljs.2019.16665
The article analyzes the foreign-born population of Lithuania, its age and ethnic composition, and periods of arrival to Lithuania. The analysis is based on the 2011 Lithuanian Population Census data. The results of the analysis show that the foreign-born population of Lithuania is very heterogeneous and has three major groups formed at different times, by different immigration factors and flows, they are different by age and ethnic composition. Most of foreign-born population of Lithuania is formed during the Soviet era - those who arrived from the former USSR. Among them the majority are of Russians, but a quite large part - Lithuanians who arrived since the mid-sixties of 20th century (children of deportees). The youngest generation of the emerging foreign-born generation is from Western European countries.
Lithuanian Journal of Statistics, Volume 58, pp 26-38; https://doi.org/10.15388/ljs.2019.16668
We propose a methodology for estimating the cost of the basic needs and applying it on the data for Lithuania in a decade after the EU accession (2006-2016). The basic food costs account for the minimal nutrition requirements, while the cost of other needs is estimated in relative terms, taking actual consumption patterns in the population into account. A reduction in the cost of the basic needs for additional members of the household is accounted for by a specially constructed consumption-based equivalence scale estimated on the HBS data. We show that the cost of the basic needs in Lithuania is close to the relative at-risk-of-poverty line (at 60% of the median equivalized disposable income) for a single adult but exceeds it for larger households. The share of people with income below the basic needs’ cost was above the relative at-risk-of-poverty levels in the EU-SILC data for all years, except of 2016. Albeit, the actual level might be lower due to the under-reporting of shadow income in the EU-SILC. Ability to meet basic needs and related absolute poverty indicators shows anti-cyclical dynamics in times of the economic growth and recession. Children are consistently the most deprived group of the Lithuanian population when it comes to meeting the basic needs. The official absolute poverty indicator used in Lithuania under-estimates the cost of the basic needs for households with more than one member.
Lithuanian Journal of Statistics, Volume 58, pp 16-25; https://doi.org/10.15388/ljs.2019.16666
This paper focuses on the quality of household age distribution from two surveys in developing countries. Age and sex data serve as the base population for the estimation of demographic parameters (fertility, mortality, etc.) and other socio-economic indicators. The ultimate objective is to evaluate the age and sex data from two surveys to determine the one with better age and sex reporting that may provide quality base populations for the estimation of demographic parameters and socioeconomic indicators. Algebraic methods were applied to the data retrieved from the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). The overall results show that the quality of data from the two surveys is poor. It is observed that age and sex data from the Nigerian DHS appear to be better than that of MICS while in Bangladesh, Malawi, and Nepal the reverse is the case based on the Joint Scores (JS). The result further shows that Malawi with high literacy respondents had better JS than the other countries indicating that the level of education may be one of the determinants of the quality of age and sex data. Therefore, it is recommended that care and caution should be taken during data collection to reduce the effect of misreporting of age and the usual practice of eliciting vital records of the respondents such as age from the head of the household instead of birth certificates should be discouraged. More importantly, evaluation of age and sex data from different surveys should be done before usage to ascertain the survey with a better quality of data without always presuming that one survey is of better quality than the other.