destination country Cho (2001) ARIMAX (adjusted ARIMA) Hong Kong tourist arrivals ARIMA residuals of GDP/GNP, rate, money supply, Consumer Price Index (CPI), imports, exports, and discount rate - 1/1975 (univariate) ARIMA, Exponential Smoothing ARIMAX is the best forecasting method for Japan, while univariate ARIMA is the best predictor for the US and UK Akal (2004) AR(I)MAX Tourism revenues for Turkey Tourist arrivals Annual - 1963 - 2001 Static Regression AR(I)MAX outperformed the simple econometric technique Petmezas, & Santamaria (2012) (*) ARIMA (1, 1, 1) Tourist numbers to Greece levels, Consumer Price Indices (CPI), and Consumer Confidence Indicators of the country of origin Monthly - Exponential smoothing models ARIMA(1,1,1) performs better ShuiKi, ShinHuei, & ChiKeung (2013) SARIMA Tourist arrivals to Hong Kong Monthly Seasonal moving average model & model. SARIMA pefrorms better Baldigara and Mamula (2015) SARIMA German tourist flows to Croatia - 2003 - 2012 No comparison is adequate Hassani, Silva, Filis, & Gupta (2017) Univariate approach / Parametric and forecasting techniques, including ARIMA tourist arrivals to European countries ETS, NN, TBATS, ARFIMA, MA, WMA, SSA-R and SSA-V No single model outperforms others in all cases. Neural Networks and ARFIMA have the worst performance (*) Gounopoulos, Petmezas, & Santamaria (2012) use of unemployment and consumer confidence as alternative proxies for the state of the economy in the country of origin. Although no studies have yet to document the impact of macroeconomic shocks on future tourists’ arrivals, reviewing the wider literature on the impact of unemployment, changes in the tourists’ cost of living, and consumer confidence could provide useful inferences on potential effects to international tourism flows.
Witt & Witt (1992) ADLM France, Germany, UK, and US tourist arrivals Income in origin country, prices, exchange rate, travel cost (airfare), travel cost to substitute destinations, currency restrictions, dummy variables Annual - 1965 - 1980 Naive 1,2 AR, Exponential Smoothing, ARIMA Trends Syriopoulos (1995) countries tourism Y/P C RC ER D DT(ST) income in origin country, relative tourism price, exchange rate, dummy variable, deterministic (linear) trend (stochastic trend) Annual - 1962 - 1987 - Song, Romilly, and Liu (2000) ECM UK outbound tourism - total or per capita tourism visits from the origin country Income in origin country, relative price, substitute price, the preference index (incorporates social, cultural and psychological influences on tourists' decisions), dummy variable Annual - 1965 - 1994 Naive, AR(1), ARMA, VAR ECM model has the best overall forecasting performance Shan & Wilson (2001) VAR China tourist arrivals Tourist arrivals, income in origin country, relative price, exports, imports, exchange rate - 1/1987 - 1/1998 - (2001) ADLM Balearic Islands tourist arrivals Exchange rate, Other economic indicators - 1/1975 - Naïve ARIMA Song, Witt & Jensen (2003) ECM - Two ECMs, one based on the WB and the other on the JML Denmark number of nights spent by tourists Real private consumption expenditure per capita in origin country, the real cost of living for tourists (adjusted by the exchange rate), prices in substitute destinations, travel cost (airfare), dummy variables (oil crisis, Gulf War, German unification, Chernobyl/U.S. bombing of Libya Annual - 1969 - 1997 Static regression, ADLM, unrestricted VAR, TVP, and two univariate time-series models (ARIMA and no-change model) The TVP model generates the most accurate forecasts. The TVP and static models generate the most accurate forecasts. For 3- and forecasts the static model is ranked first. Nadal
Witt, Song, and Louvieris (2003) Denmark number of nights spent by tourists Real private consumption expenditure per capita, the real cost of living for tourists (adjusted by the exchange rate), prices in substitute destinations, travel cost (airfare), dummy variables (oil crisis, Gulf War, German unification, Chernobyl/U.S. bombing of Libya) Annual - 1969 - 1999 Static Regression, VAR, the Time-Varying Parameter (TVP) approach - The univariate timeseries models comprise an ARIMA model and the no-change model For both 2- and forecasting horizons,the VAR model generates the most forecasts. For a forecasting horizon,the TVP model and the reduced ADLM generate the most unbiased forecasts Song, Witt & Li (2003) (1) ADLM, ECM, and ECM Thailand tourist arrivals Income in origin country, relative tourism price, substitute tourism price, trade volume, habit persistence, and dummy variables (two oil crises, Visit Thailand Year’ campaign, Asian financial crisis, Seoul Olympics, and student demonstrations in Korea in 1980) Annual - 2000 ARIMA, Naive, TVP ARIMA produces the most modest prediction (with the exception of Singapore), while the JML-ECM generates the most optimistic forecasts. Habit persistence is the most important factor. Nadal, Font, & Rossello (2004) (2) ECM Intra-year variation in arrivals to the Balearic Islands Y/P ER RC income in origin country, exchange rate - 1982 - 2001 - Dritsakis (2004) VAR Long-run economic growth of Greece (real gross domestic product & real effective exchange rate) International tourism earnings - Significant causality ahead
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.
Claveria, Monte, & Torra (2015) (1) Radial basis function neural networks Tourist arrivals to Catalonia Correlations in the evolution of inbound tourism demand Monthly - 1/2001 - 7/2012 Multi-layer perceptron and Elman recursive neural networks Radial basis function neural networks performs better Claveria, Torra, & Monte (2016) (2) Machine learning techniques - Support Vector Regression (SVR) tourist arrivals to key Spanish regions Monthly - 1/1999 to 3/2014 Neural Network (NN), ARMA SVR with a Gaussian radial basis function kernel performs better than the other models Claveria, Monte, & Torra (2016) (3) Extension of the Gaussian process regression model for forecasting Tourist arrivals to all Spanish regions in tourist arrivals to all markets Monthly data - 1/1999 - 3/2014 Standard neural network Extension of Gaussian outperforms the benchmark Kummong & Supratid (2016) (4) Discrete Wavelet - Combination of DWD and nonlinear neural network with exogenous input (NARX) Thailand tourist arrivals Relative price with respect to 6 top-ranked tourist countries (exogenous) NARX and other neural network approaches (GRNN, Elman’s RNN and BPNN) more suitable for projections Yu, Wang, Gao, Tang (2017) (5) Seasonal trend integrated moving averages with Dendritic Neural Network (SA-D model) - Combined SARIMA and DNN Inbound tourists - Japan 1/2009 - 12/2015 Dendritic Neural Network (DNN) SA-D performs better Claveria, Monte, & Torra (2015) (1) A way of using the common trends in tourist arrivals from different visitor markets and assessing its performance. Claveria, Torra, & Monte (2016) (3) The main contribution of this study to the previous literature on tourism demand forecasting is the evaluation of the performance of several ML techniques by means of an iterated multi-step forecasting comparison. NARX