Some considerations about tourist arrivals and the COVID-19 pandemic – evidence from Slovenia and Croatia

Figures & data

Table 1. Coronavirus (COVID-19) literature review desk-research.

Author(s)	Pinpoint	Highlights of the research	Ruling
(Alsayed et al., 2020)	Prediction	The mathematical modelling on a genetic algorithm Predicting COVID-19 peaks	Ex post
(Gallego et al., 2020)	Unpredictable	Unpredictable future events The territory of the COVID-19	Ex post
(Gričar, 2019)	Forecast	Regression analysis Tourist health and treatment	Ex ante
(Hugo, 2021)	Prognosis	Innovation of tourism	Ex post
(Kostynets et al., 2020)	Shocks	Pandemic and other shocks that consequences tourism demand	Ex post, & ex ante
(Kostynets et al., 2021)	Pent-up demand	Influencing demand regarding COVID-19 pandemic	Ex post
(Wang et al., 2021)	Prediction, Forecast	RegARMA modelling Railway passengers and gross domestic product	Ex post
(Wong et al., 2007)	Forecasting	Modelling tourism demand ARIMA	Ex post
(Yang et al., 2018)	Predictive	Several methods plus ARIMA Time series data	Ex post
(Yue et al., 2020b)	Prediction	ARIMA Trending breaks and shocks	Ex post
(Yue et al., 2020a)	Prediction	New predictable research Modelling process in tourism	Ex post
(Zhang, Song et al., 2021)	Prediction	Post COVID-19 effects Mixed study methods	Ex post

Source: Authors compilation, 2021.

Table 2. Values (coefficients) of the variables studied, dependent variable number of tourist arrivals, January 2008–December 2016. $N=108.$

Regressor, constant	Coefficient*	$\mathrm{t}$-statistics	$p$ value	Incidences,
Constant	97,281	2.48	0.01	/
Cholera	−56,046	−0.84	0.40	1
Salmonella	292	1.33	0.19	4515
Dysentery	1795	0.60	0.55	242
E.coli	3619	3.46	0.00	1487
Campylobacter	1760	8.76	0.00	9664
Enterococcus and Staphylokokkus	320	0.74	0.46	7155
Botulism	3163	0.12	0.91	4
Clostridium and B. cereus	−89	−0.39	0.70	1360
Viruses	−34	−2.31	0.02	175,569
Listeria	10,628	1.62	0.11	94
Sepsis	−225	−0.47	0.64	8132
Hepatitis A	1280	0.23	0.82	113
Statistical values of the model
F statistics	20.68
D-W statistics	1.73
Deterministic coefficient	0.69

Source: Gričar (2019).

Figure 1. Comparison of COVID-19 cases and tourist arrivals in Croatia for 2020, daily data, fixed index March 5, 2020 = 100.

Source: Author's calculations and rectifications from ECDC (2021) and CBS (2021).

Figure 2. Comparison of COVID-19 cases and tourist arrivals in Slovenia for 2020, daily data, fixed index March 5, 2020 = 100.

Source: Author's calculations and rectifications from ECDC (2021) and SORS (2021).

Table 3. Misspecification test, daily data, fixed index, March 5, 2020 = 100.

		$I(0)$		$I(1),$ ln
Test Variable	Standard deviation	ADF (${\chi }^2$)	Jarque – Bera normality test	ADF (${\chi }^2$)	Jarque – Bera normality test
Tourist arrivals Croatia	869.52	−1.41	65.89***	−16.79***	67520.80***
Tourist arrivals Slovenia	362.06	−1.11	37.81***	−16.79***	194049.00***
Positive COVID-19 cases Croatia	112760	1.89	287.37***	−3.23**	41.73***
Positive COVID-19 cases Slovenia	62793	−0.09	229.74***	−3.59**	85.71***

Source: The author's compilation based on data is obtained from CBS (2021), ECDC (2021), SORS (2021).

Note: A.D.F. – Augmented Dickey-Fuller test, ***, ** – 1%, 5% significance level, $I(0)$ – levels, $I(1)$ – first difference, ln – logarithm.

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