Forecasting emergency department arrivals using INGARCH models

Health Economics Review

Table 3 Parameters estimates and standard deviation (in parenthesis) for the model in Eq. (7)

	\(\mathrm{\alpha }\)	\(\beta\)	\(\theta\)	\({\gamma }_{Winter}\)	\({\gamma }_{Monday}\)	\({\gamma }_{Weekend}\)	\({\gamma }_{COVID}\)	LogLik	AIC	BIC
Zhu [38]	0.27 ***	0.68 ***	235.14 ***	-0.00 ***	0.18 ***	-0.07 ***	-0.03 ***	-10,726	21,467	21,507
Zhu [38]	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	-10,726	21,467	21,507
Xu et al. [37]	0.26 ***	0.67 ***	0.37 ***	0.37 **	0.17 ***	-0.07 ***	-0.03 ***	-10,723	21,461	21,500
Xu et al. [37]	(0.01)	(0.01)	(0.01)	(0.00)	(0.01)	(0.00)	(0.00)	-10,723	21,461	21,500
Poisson	0.18 ***	0.77 ***		-0.00 ***	0.17 ***	-0.07 ***	-0.02 ***	-11,537	23,086	23,120
Poisson	(0.01)	(0.01)		(0.00)	(0.00)	(0.00)	(0.00)	-11,537	23,086	23,120

***, **, and * indicate that the parameter is significant at 1%, 5% and 10%, respectively. The parameters are estimated using the full sample. Parameter \(\omega\) in Eq. (7) is obtained by weighting, by (1 \(-\mathrm{\alpha }-\upbeta\)), the mean of the number of arrivals on days not affected by the dummies

ISSN: 2191-1991