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Continuous‐time modelling of irregularly spaced panel data using a cubic spline model
Author(s) -
Chow SyMiin,
Zhang Guangjian
Publication year - 2008
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2007.00379.x
Subject(s) - nonparametric statistics , spline (mechanical) , interpolation (computer graphics) , computer science , smoothing spline , representation (politics) , spline interpolation , algorithm , process (computing) , panel data , data mining , data set , mathematics , econometrics , statistics , artificial intelligence , engineering , motion (physics) , structural engineering , politics , bilinear interpolation , law , political science , operating system
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.