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Continuous‐time modelling of irregularly spaced panel data using a cubic spline model

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  • Sy‐Miin Chow
  • Guangjian Zhang

Abstract

Continuous‐time modelling remains a somewhat ‘idealized’ representation tool. Even though conceptualizing a dynamic process as a continuous process has clear appeal from a theoretical standpoint, practical tools that allow researchers to effectively map an idealized continuous model onto a set of discrete‐time observed data are still lacking observed data. Irregularly spaced longitudinal data frequently arise in empirical settings because of the prevalence of longitudinal studies with partially randomized measurement intervals and other related designs. We present a practical approach that capitalizes on a nonparametric spline interpolation approach to impute the gaps in irregularly spaced panel data. Simulated and empirical examples are provided to demonstrate the applicability of the proposed approach to studies of group‐based dynamics using panel data.

Suggested Citation

  • Sy‐Miin Chow & Guangjian Zhang, 2008. "Continuous‐time modelling of irregularly spaced panel data using a cubic spline model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 131-154, February.
    • Handle: RePEc:bla:stanee:v:62:y:2008:i:1:p:131-154
    DOI: 10.1111/j.1467-9574.2007.00379.x
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    References listed on IDEAS

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    1. Bergstrom, A. R., 1988. "The History of Continuous-Time Econometric Models," Econometric Theory, Cambridge University Press, vol. 4(3), pages 365-383, December.
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    3. Johan Oud & Robert Jansen, 2000. "Continuous time state space modeling of panel data by means of sem," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 199-215, June.
    4. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
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