Migration is a space and time dependent phenomenon. Traditional geographical migration models have considered the distance between source and destination countries or have applied suitable normalizations to treat the correlation among migratory flows. To disentangle cross-sectional dependence, spatial correlation is explored in mainly two directions. First, migratory flows from "neighbouring" countries are considered to be directly interconnected. Second, a set for exogenous drivers are allowed to be correlated among the different economic units. Swiss immigration, from 153 source countries from 1981 to 2011, is modelled using a dynamic spatial econometric model able to capture both path-dependency and spatial interactions. An out-of-sample forecasting, performed to assess the model's accuracy, confirms the crucial role played by the spatial terms over the dynamic ones. International Migration Dynamic Spatial Panel Model Spatial Autocorrelation. JEL Classification: F22 C21 C23 J61 * The research leading to these results has received funding from the Swiss National Science Foundation in the context of the NCCR on the move.
Summary
We model complex trend–seasonal interactions within a Bayesian framework. The contribution divides into two parts. First, it proves, via a set of simulations, that a semiparametric specification of the interplay between the seasonal cycle and the global time trend outperforms parametric and non‐parametric alternatives when the seasonal behaviour is represented by Fourier series of order bigger than 1. Second, the paper uses a Bayesian framework to forecast Swiss immigration, merging the simulations’ outcome with a set of priors derived from alternative hypotheses about the future number of incomers. The result is an effective symbiosis between Bayesian probability and semiparametric flexibility that can reconcile past observations with unprecedented expectations.
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