Wong, Song, Demand for Income level of origin Annual unrestricted Univariate & Chon VAR Hong Kong country, the relative - 1973 - VAR models Bayesian (2006) (3) tourism by cost of living, the 1996 VAR residents Substitute Price (BVAR) the from six variable best major origin performing countries model Kasimati Economic International tourist Annual - No direct (2011) - growth of arrivals data - causality Greece (GDP, model - Granger Tourism Total tourism Annual A second All variables (2012) - expenditure expenditure (TE) and data - model treated return to and growth real output (RGDP) leisure travel their in Greece with real effective and tourism model exchange rate (REER) spending equilibrium (LTS), business travel and tourism spending (BTS), RGDP and REER as separate inputs Tourist Macroeconomic SARIMA SARMAX arrivals to indicators: Industrial data outperforms Degiannakis, (with Greece production, consumer SARIMA - Eeckels, & from seven price index (CPI), CC No single Filis (2016) key origin index, BC index, variable Economic PUI, and improves the the consumer price forecasting data) - differentials (CPDs) accuracy for between Greece and all cases - data by the origin countries The CC origin (estimated using the index is country Greek CPI) - critical for Canada and the UK - The price level for France and Germany - Income for Italy and Spain - Economic policy uncertainty for the USA. Zhu, Lim, Frank Tourist Income, relative price, Product Frank model Xie, & Wu flows to and transportation data - copula, performs (2016) (4) Singapore cost - Exponential 1995 - ARDL-ECM better model from 6 smoothing is used to 2013 countries predict them Song, Witt & Li (2003) (1) The relatively poor forecasting performance of the VAR model for all forecasting horizons other than 4 years ahead may have arisen because it is the only causal model included in the forecasting comparison which generates ex ante forecasts. Alternatively, however, the low ranking may suggest that the distinction between endogenous and exogenous variables in tourism demand forecasting models is fairly clear, or at least that allowing for the distinction not to be clear does not result in more accurate forecasts except perhaps in the longer term.
Wong, Song, & Chon (2006) (3) VAR Demand for Hong Kong tourism by residents from six major origin countries Income level of origin country, the relative cost of living, the Substitute Price variable Annual - 1973 - 1996 unrestricted VAR models Univariate Bayesian VAR (BVAR) the best performing model Kasimati (2011) - model - Granger Economic growth of Greece (GDP, International tourist arrivals Annual data - - No direct causality (2012) - model Tourism expenditure and growth in Greece Total tourism expenditure (TE) and real output (RGDP) with real effective exchange rate (REER) Annual data - A second model treated leisure travel and tourism spending (LTS), business travel and tourism spending (BTS), RGDP and REER as separate inputs All variables return to their equilibrium Degiannakis, Eeckels, & Filis (2016) (with Tourist arrivals to Greece from seven key origin data) - data by origin country Macroeconomic indicators: Industrial production, consumer price index (CPI), CC index, BC index, Economic PUI, and the consumer price differentials (CPDs) between Greece and the origin countries (estimated using the Greek CPI) - data SARIMA SARMAX outperforms SARIMA - No single variable improves the forecasting accuracy for all cases - The CC index is critical for Canada and the UK - The price level for France and Germany - Income for Italy and Spain - Economic policy uncertainty for the USA. Zhu, Lim, Xie, & Wu (2016) (4) Frank model Tourist flows to Singapore from 6 countries Income, relative price, and transportation cost - Exponential smoothing is used to predict them data - 1995 - 2013 Product copula, ARDL-ECM Frank model performs better Song, Witt & Li (2003) (1) The relatively poor forecasting performance of the VAR model for all forecasting horizons other than 4 years ahead may have arisen because it is the only causal model included in the forecasting comparison which generates ex ante forecasts. Alternatively, however, the low ranking may suggest that the distinction between endogenous and exogenous variables in tourism demand forecasting models is fairly clear, or at least that allowing for the distinction not to be clear does not result in more accurate forecasts except perhaps in the longer term.
I1_2_FinalReport_F.docx