Bandingkan kaedah

	Tourism Demand Elasticity Modeling	Tourism Demand Forecasting
Bidang	Tourism Hospitality	Tourism Hospitality
Keluarga	Regression model	Regression model
Tahun asal	1994	2008
Pengasas	Geoffrey I. Crouch	Haiyan Song; Gang Li; Stephen F. Witt
Jenis	Econometric demand-elasticity estimation	Predictive time-series and econometric demand models
Sumber perintis	Crouch, G. I. (1994). The Study of International Tourism Demand: A Review of Findings. Journal of Travel Research, 33(1), 12-23.	Song, H., & Li, G. (2008). Tourism demand modelling and forecasting - A review of recent research. Tourism Management, 29(2), 203-220.
Alias	Tourism Income Elasticity, Tourism Price Elasticity, Elasticity of International Tourism Demand, Tourism Demand Sensitivity Analysis	Tourist Arrivals Forecasting, SARIMA Tourism Forecasting, Tourism Demand Modelling and Forecasting, Econometric Tourism Forecasting
Berkaitan	4	4
Ringkasan	Tourism demand elasticity modeling estimates how responsive tourist demand is to changes in its key drivers, above all source-market income and the price of travel. The income elasticity measures the percentage change in demand for a one-percent change in income, and the price elasticity does the same for price; both are recovered as coefficients in econometric demand models, most simply a log-linear regression where the coefficients read directly as elasticities. Geoffrey Crouch's mid-1990s surveys of the international tourism demand literature consolidated decades of such estimates, showing that tourism is typically income-elastic — a luxury that grows faster than income — and price-sensitive, with values that vary systematically across markets and methods. Later meta-analyses, such as Peng, Song, Crouch, and Witt's, quantified that variation across hundreds of studies.	Tourism demand forecasting predicts future tourist arrivals, overnight stays, or expenditure from historical data, supporting planning by destinations, airlines, hotels, and policymakers. The field spans two broad model families. Time-series models such as seasonal ARIMA (SARIMA) extrapolate the patterns embedded in the demand series itself — trend, seasonality, and autocorrelation — without explanatory variables. Econometric models such as autoregressive distributed lag models (ADLM) and error-correction models relate demand to drivers like income, relative prices, and exchange rates, allowing both forecasting and policy analysis. Haiyan Song and Gang Li's influential 2008 review in Tourism Management synthesized this literature, documenting the proliferation of methods since 2000 and emphasizing rigorous out-of-sample evaluation. Their work, with Stephen Witt, helped make tourism demand forecasting a methodologically mature subfield.