Cross-border mobility responses to COVID-19 in Europe: new evidence from facebook data

Table 1 MAE comparison of the different models with or without directional priors and dummies

	Linear	KNN	G-Boost	MLP	Linear	KNN	G-Boost	MLP
	Panel A: No dummies - No prior				Panel B: No dummies - Priors
avg MAE	0.194	0.018	0.073	0.051	0.201	0.019	0.042	0.057
std MAE	(0.009)	(0.001)	(0.001)	(0.005)	(0.005)	(0.001)	(0.001)	(0.003)
avg RMSE	0.285	0.042	0.106	0.083	0.287	0.043	0.064	0.089
std RMSE	(0.017)	(0.009)	(0.003)	(0.011)	(0.009)	(0.005)	(0.002)	(0.008)
	Panel C: Dummies - No prior				Panel D: Dummies - Priors
avg MAE	0.135	0.020	0.050	0.041	0.134	0.020	0.049	0.038
std MAE	(0.005)	(0.001)	(0.002)	(0.003)	(0.005)	(0.001)	(0.001)	(0.003)
avg RMSE	0.210	0.045	0.081	0.068	0.203	0.047	0.077	0.064
std RMSE	(0.010)	(0.006)	(0.006)	(0.006)	(0.009)	(0.005)	(0.002)	(0.005)

Note: The table compares the performances of the 4 different approaches (Linear, KNN, G-Boost and MLP) with and without directional priors (ω_i→j,0), and with or without day/corridor dummies (d_t and d_ij). Errors are computed from a 10-fold cross-validation on the whole sample

ISSN: 1744-8603