Sigurd Dyrting , Charles Darwin University
Age-specific migration intensities often display irregularities that need to be removed by graduation but two current methods for doing so, parametric model migration schedules and non-parametric kernel regression, have their limitations. This paper introduces P-TOPALS, a relational method for smoothing migration data that combines both parametric and non-parametric approaches. I adapt de Beer's TOPALS framework to migration data and combine it with penalised splines to give a method that frees the user from choosing the optimal number and position of knots and which can be solved using linear techniques. I compare it to smoothing by model migration schedules and kernel regression using Australian census data at both the aggregate and state level. I find that P-TOPALS combines the strengths of both student model migration schedules and kernel regression to allow a good estimation of the high-curvature portion of the curve at young adult ages as well as a sensitive modelling of intensities beyond the labour force peak.
Presented in Session 33. Methods for Migration Research