<abstract xmlns="http://www.w3.org/1999/xhtml">

The primary goal of this research was to estimate the quantile of a conditional distribution using a semi-parametric approach in the presence of randomly missing data, where the predictor variable belongs to a semi-metric space. The authors assumed a single index structure to link the explanatory and response variable. First, a kernel estimator was proposed for the conditional distribution function, assuming that the data were selected from a stationary process with missing data at random (MAR). By imposing certain general conditions, the study established the model’s uniform almost complete consistencies with convergence rates.
</abstract>

University Djillali Liabes of Sidi Bel Abbes

Department of Mathematics and Computer Science

Single Functional Index Quantile Regression for Functional Data with Missing Data at Random

<abstract xmlns="http://www.w3.org/1999/xhtml">

The aim of this article was to analyse functional data of the number of hospitalised individuals, intensive care patients, positive COVID-19 tests, deaths and convalescents during the second wave of the COVID-19 pandemic in Poland. For this purpose, firstly the author convert data of sixteen voivodeships to smooth functions, and then used the principal component analysis and multiple function-on-function linear regression model to predict the number of hospitalised and intensive care patients due to the COVID-19 infection during the second wave of the pandemic. Finally, the results were compared with those previously obtained for the combined data of the second and third wave of the COVID-19 pandemic in Poland (Hęćka, 2023).
</abstract>

The Second Wave of the COVID-19 Pandemic in Poland – Characterised Using FDA Methods

<abstract xmlns="http://www.w3.org/1999/xhtml">

The article considers the problems of young people aged 20-24 on the labour market affected by unemployment in European Union countries. Unemployment is one of the most important economic and social problems, which at the same time constitutes one of the biggest measures characterising the condition of the economy. The diversity of the economic situation in EU countries directly affects young people, an individual group of people entering the labour market and have little or no professional experience. At the same time, they are ready to start work, facing great difficulties in entering the market, influenced by socio-economic as well as demographic factors which directly and indirectly affect employment. Considering the above premise, the aim of the article was to identify the determinants of unemployment of young people aged 20-24 in the EU. The study used data from two years: 2010 and 2020, and applied multiple regression. Statistical data were taken from Eurostat databases. The study allowed to examine the dependence of the influence of individual socioeconomic as well as demographic factors on youth unemployment. The study found that the multivariate regression showed that factors related to young people's participation in education and training (including the NEET rate) relative to labour market status, as well as social inclusion, had a significant impact on the unemployment studied. Over the decade, a decrease was seen in unemployment in most EU member states, in as many as 19 countries, while the remaining eight countries showed an increase.
</abstract>

Evaluation of the Labour Market Situation of Young People in EU Countries – The Multiple Regression Approach

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Econometrics

Volume 27 (2023): Issue 3 (September 2023)

Single Functional Index Quantile Regression for Functional Data with Missing Data at Random

The Second Wave of the COVID-19 Pandemic in Poland – Characterised Using FDA Methods

Evaluation of the Labour Market Situation of Young People in EU Countries – The Multiple Regression Approach

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