IDEAS home Printed from https://ideas.repec.org/p/fln/wpaper/010.html
   My bibliography  Save this paper

Estimation of a Two-Equation Panel Model with Mixed Continuous and Ordered Categorical Outcomes and Missing Data

Author

Listed:
  • Martin Spieß

    (International Institute of Management, Europa-Universität Flensburg)

Abstract

No abstract is available for this item.

Suggested Citation

  • Martin Spieß, 2006. "Estimation of a Two-Equation Panel Model with Mixed Continuous and Ordered Categorical Outcomes and Missing Data," Discussion Papers 010, Europa-Universität Flensburg, International Institute of Management.
    • Handle: RePEc:fln:wpaper:010
    as

    Download full text from publisher

    File URL: https://www.uni-flensburg.de/fileadmin/content/institute/iim/dokumente/forschung/discussion-papers/10-estimation-of-a-two-equation-panel-spie.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beth Reboussin & Kung-Yee Liang, 1998. "An estimating equations approach for the LISCOMP model," Psychometrika, Springer;The Psychometric Society, vol. 63(2), pages 165-182, June.
    2. Spiess, Martin & Hamerle, Alfred, 2000. "A comparison of different methods for the estimation of regression models with correlated binary responses," Computational Statistics & Data Analysis, Elsevier, vol. 33(4), pages 439-455, June.
    3. Gueorguieva R. V. & Agresti A., 2001. "A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1102-1112, September.
    4. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
    5. Bengt Muthén & Albert Satorra, 1995. "Technical aspects of Muthén's liscomp approach to estimation of latent variable relations with a comprehensive measurement model," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 489-503, December.
    6. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
    7. Richard A. Easterlin (ed.), 2002. "Happiness in Economics," Books, Edward Elgar Publishing, number 2479, March.
    8. SOEP Group, 2001. "The German Socio-Economic Panel (GSOEP) after More than 15 Years: Overview," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 70(1), pages 7-14.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
    2. Martin Spiess & Pascal Jordan & Mike Wendt, 2019. "Simplified Estimation and Testing in Unbalanced Repeated Measures Designs," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 212-235, March.
    3. Gerd Grözinger, 2014. "Krise der Eurozone - Was tun?," Discussion Papers 022, Europa-Universität Flensburg, International Institute of Management.

    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. Martin Spieß, 2006. "On the Returns to Occupational Qualification in Terms of Subjective and Objective Variables: A GEE-type Approach to the Estimation of Two-Equation Panel Models," Discussion Papers of DIW Berlin 564, DIW Berlin, German Institute for Economic Research.
    2. Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
    3. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
    4. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
    5. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
    6. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    7. Martin Spiess, 2000. "A Generalized Estimating/Pseudo-Score Equations Approach for the Estimation of Structural Equation Models," Discussion Papers of DIW Berlin 218, DIW Berlin, German Institute for Economic Research.
    8. Hoshino, Takahiro, 2008. "Bayesian significance testing and multiple comparisons from MCMC outputs," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3543-3559, March.
    9. Hao Sun & Emily Berg & Zhengyuan Zhu, 2022. "Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model," Biometrics, The International Biometric Society, vol. 78(4), pages 1555-1565, December.
    10. Hao Bai & Yuan Zhong & Xin Gao & Wei Xu, 2020. "Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method," Stats, MDPI, vol. 3(3), pages 1-18, July.
    11. Li Cai, 2010. "High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 33-57, March.
    12. D. Gunzler & W. Tang & N. Lu & P. Wu & X. Tu, 2014. "A Class of Distribution-Free Models for Longitudinal Mediation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 543-568, October.
    13. Bruno S. Frey & Anthony Gullo, 2021. "Does Sports Make People Happier, or Do Happy People More Sports?," Journal of Sports Economics, , vol. 22(4), pages 432-458, May.
    14. Bergemann, Annette & Mertens, Antje, 2004. "Job Stability Trends, Layoffs, and Transitions to Unemployment: An Empirical Analysis for West Germany," IZA Discussion Papers 1368, Institute of Labor Economics (IZA).
    15. Constant, Amelie F. & Gataullina, Liliya & Zimmermann, Klaus F., 2009. "Ethnosizing immigrants," Journal of Economic Behavior & Organization, Elsevier, vol. 69(3), pages 274-287, March.
    16. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    17. Jon Wisman, 2011. "Inequality, Social Respectability, Political Power, and Environmental Devastation," Journal of Economic Issues, Taylor & Francis Journals, vol. 45(4), pages 877-900.
    18. Joachim R. Frick & Markus M. Grabka & Olaf Groh-Samberg, 2007. "Economic Gains from Publicly Provided Education in Germany," Discussion Papers of DIW Berlin 709, DIW Berlin, German Institute for Economic Research.
    19. Anna Carabelli & Mario Cedrini, 2011. "The Economic Problem of Happiness: Keynes on Happiness and Economics," Forum for Social Economics, Springer;The Association for Social Economics, vol. 40(3), pages 335-359, October.
    20. Thomas Wai-Kee Yuen & Winnie Wan-Ling Chu, 2015. "Happiness in ASEAN member states," International Journal of Happiness and Development, Inderscience Enterprises Ltd, vol. 2(1), pages 69-83.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fln:wpaper:010. 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: Kathrin Thomsen (email available below). General contact details of provider: https://edirc.repec.org/data/iiflede.html .

    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.