NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries
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DOI: 10.1007/s11205-021-02813-5
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Cited by:
- Fulvia Pennoni & Francesco Bartolucci & Silvia Pandolfi, 2024. "Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 568-589, November.
- Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
- Oğuz Başol & Hüseyin Sevgi & Esin Cumhur Yalçın, 2023. "The Effect of Digitalization on Youth Unemployment for EU Countries: Treat or Threat?," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
- Giorgia Casalone & Eliana Baici, 2023. "Education, Off-the-Job Vocational Training, and Early Employment Outcomes: Evidence from Italy," Merits, MDPI, vol. 3(2), pages 1-15, May.
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More about this item
Keywords
Expectation-maximization algorithm; Discrete latent variable; Hidden Markov model; Model-based clustering; Panel data; NEET; Youth unemployment;All these keywords.
JEL classification:
- C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
- J80 - Labor and Demographic Economics - - Labor Standards - - - General
- J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
- J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
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