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Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

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  • Daniel L. Millimet
  • Ian K. McDonough

Abstract

With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators breakdown when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non-uniform intervals and no solution is currently available when time-varying covariates are included in the model. In this paper, we propose several new estimators for dynamic panel data models when data are irregularly spaced and compare their finite sample performance to the naïve application of existing estimators. We illustrate the practical importance of this issue by turning to two applications on early childhood development.
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Suggested Citation

  • Daniel L. Millimet & Ian K. McDonough, 2017. "Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 725-743, June.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:4:p:725-743
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    Cited by:

    1. Zhang, Xiaoge & Chen, Maolong, 2023. "Indirect inference approach to estimating dynamic panel data models with irregular spacing," Economics Letters, Elsevier, vol. 226(C).
    2. Kasem Kunasri & Manh Hung Do & Trung Thanh Nguyen, 2025. "The Role of Farming Efficiency in Rural Transformation: Insights From Long‐Term Panel Data for Thailand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 69(4), pages 911-933, October.
    3. Hendrik Thiel & Stephan L. Thomsen, 2015. "Individual Poverty Paths and the Stability of Control-Perception," SOEPpapers on Multidisciplinary Panel Data Research 794, DIW Berlin, The German Socio-Economic Panel (SOEP).
    4. Pavlik, Jamie Bologna & Young, Andrew T., 2019. "Did technology transfer more rapidly East–West than North–South?," European Economic Review, Elsevier, vol. 119(C), pages 216-235.
    5. Vivek Ghosal & Andreas Stephan & Jan F. Weiss, 2019. "Decentralized environmental regulations and plant‐level productivity," Business Strategy and the Environment, Wiley Blackwell, vol. 28(6), pages 998-1011, September.
    6. Sasaki, Yuya & Xin, Yi, 2017. "Unequal spacing in dynamic panel data: Identification and estimation," Journal of Econometrics, Elsevier, vol. 196(2), pages 320-330.
    7. Ignace De Vos & Gerdie Everaert & Ilse Ruyssen, 2015. "Bootstrap-based bias correction and inference for dynamic panels with fixed effects," Stata Journal, StataCorp LLC, vol. 15(4), pages 986-1018, December.
    8. Owen Davis & Siavash Radpour, 2021. "Dissecting the Pandemic Retirement Surge," SCEPA publication series. 2021-05, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    9. Luisa Corrado & Roberta Distante & Majlinda Joxhe, 2019. "Body mass index and social interactions from adolescence to adulthood," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(4), pages 425-445, October.
    10. Christian Aßmann & Marcel Preising, 2020. "Bayesian estimation and model comparison for linear dynamic panel models with missing values," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 536-557, December.
    11. Khalaf, Lynda & Kichian, Maral & Saunders, Charles J. & Voia, Marcel, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Journal of Econometrics, Elsevier, vol. 220(2), pages 589-605.
    12. Cobb-Clark Deborah A. & Harmon Colm & Staneva Anita, 2021. "The bilingual gap in children's language, emotional, and pro-social development," IZA Journal of Labor Economics, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 10(1), pages 1-41, January.
    13. Linh D. Nguyen & Thanh T. Nguyen & Tung T. Nguyen & Ulrike Grote, 2025. "Health shock and indebtedness: Does having access to health insurance reduce the reliance on borrowing as a shock coping strategy?," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 47(2), pages 823-862, May.
    14. Fiona Steele & Emily Grundy, 2021. "Random effects dynamic panel models for unequally spaced multivariate categorical repeated measures: an application to child–parent exchanges of support," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 3-23, January.
    15. Hamza Bennani & Matthias Neuenkirch, 2017. "The (home) bias of European central bankers: new evidence based on speeches," Applied Economics, Taylor & Francis Journals, vol. 49(11), pages 1114-1131, March.
    16. Steele, Fiona & Grundy, Emily, 2021. "Random effects dynamic panel models for unequally-spaced multivariate categorical repeated measures: an application to child-parent exchanges of support," LSE Research Online Documents on Economics 106255, London School of Economics and Political Science, LSE Library.
    17. Badi H. Baltagi & Long Liu, 2020. "Forecasting with unbalanced panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 709-724, August.
    18. Fortin, Bernard & Yazbeck, Myra, 2015. "Peer effects, fast food consumption and adolescent weight gain," Journal of Health Economics, Elsevier, vol. 42(C), pages 125-138.
    19. Chen, Maolong & Myers, Robert J. & Hu, Chaoran, 2020. "Estimating dynamic binary choice models using irregularly spaced panel data," Economics Letters, Elsevier, vol. 192(C).
    20. Feridoon Koohi-Kamali & Amit Roy, 2021. "Environmental Shocks and Child Labor: A Panel Data Ethiopia & India," SCEPA working paper series. 2021-05, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    21. Dizioli, Allan & Pinheiro, Roberto, 2016. "Health insurance as a productive factor," Labour Economics, Elsevier, vol. 40(C), pages 1-24.
    22. Mulubrhan Amare & Priyanka Parvathi & Trung Thanh Nguyen, 2023. "Micro insights on the pathways to agricultural transformation: Comparative evidence from Southeast Asia and Sub‐Saharan Africa," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 71(1), pages 69-87, March.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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