IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/at368.html
   My bibliography  Save this paper

Adjusted Bayesian Completion Rates (ABC) Estimation

Author

Listed:
  • Barakat, Bilal Fouad

    (Austrian Academy of Sciences)

  • Dharamshi, Ameer
  • Alkema, Leontine
  • Antoninis, Manos

Abstract

Estimating school completion is crucial for monitoring SDG 4 on education, and unlike enrollment indicators, relies on household surveys. Associated data challenges include gaps between waves, conflicting estimates, age misreporting, and delayed completion. Our Adjusted Bayesian Completion Rates (ABC) model overcomes these challenges to produce the first complete and consistent time series for SDG indicator 4.1.2, by school level and sex, for 153 countries. A latent random walk process for unobserved true rates is adjusted for a range of error and variance sources, with weakly informative priors. The model appears well-calibrated and offers a meaningful improvement in predictive performance.

Suggested Citation

  • Barakat, Bilal Fouad & Dharamshi, Ameer & Alkema, Leontine & Antoninis, Manos, 2021. "Adjusted Bayesian Completion Rates (ABC) Estimation," SocArXiv at368, Center for Open Science.
    • Handle: RePEc:osf:socarx:at368
    DOI: 10.31219/osf.io/at368
    as

    Download full text from publisher

    File URL: https://osf.io/download/60085650e80d37096aa566d0/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/at368?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Monica Alexander & Leontine Alkema, 2018. "Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(15), pages 335-372.
    3. Yeojin Chung & Sophia Rabe-Hesketh & Vincent Dorie & Andrew Gelman & Jingchen Liu, 2013. "A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 685-709, October.
    4. Jonah Gabry & Daniel Simpson & Aki Vehtari & Michael Betancourt & Andrew Gelman, 2019. "Visualization in Bayesian workflow," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 389-402, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ameer Dharamshi & Bilal Barakat & Leontine Alkema & Manos Antoninis, 2022. "A Bayesian model for estimating Sustainable Development Goal indicator 4.1.2: School completion rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1822-1864, November.
    2. Andrew J Tanentzap & Samuel Cottingham & Jérémy Fonvielle & Isobel Riley & Lucy M Walker & Samuel G Woodman & Danai Kontou & Christian M Pichler & Erwin Reisner & Laurent Lebreton, 2021. "Microplastics and anthropogenic fibre concentrations in lakes reflect surrounding land use," PLOS Biology, Public Library of Science, vol. 19(9), pages 1-18, September.
    3. Aldo Gardini & Enrico Fabrizi & Carlo Trivisano, 2022. "Poverty and inequality mapping based on a unit‐level log‐normal mixture model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2073-2096, October.
    4. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    5. Zhang, Yunchang & Fricker, Jon D., 2021. "Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model," Transport Policy, Elsevier, vol. 103(C), pages 11-20.
    6. Frederik Banis & Henrik Madsen & Niels K. Poulsen & Daniela Guericke, 2020. "Prosumer Response Estimation Using SINDyc in Conjunction with Markov-Chain Monte-Carlo Sampling," Energies, MDPI, vol. 13(12), pages 1-16, June.
    7. Rico Krueger & Taha H. Rashidi & Akshay Vij, 2020. "X vs. Y: an analysis of intergenerational differences in transport mode use among young adults," Transportation, Springer, vol. 47(5), pages 2203-2231, October.
    8. Jair Andrade & Jim Duggan, 2021. "A Bayesian approach to calibrate system dynamics models using Hamiltonian Monte Carlo," System Dynamics Review, System Dynamics Society, vol. 37(4), pages 283-309, October.
    9. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    10. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    11. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    12. Heinrich, Torsten & Yang, Jangho & Dai, Shuanping, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," MPRA Paper 105011, University Library of Munich, Germany.
    13. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    14. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    15. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    16. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    17. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.
    18. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    19. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    20. D. Fouskakis & G. Petrakos & I. Rotous, 2020. "A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 255-270, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:at368. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.