Сравнение на методи

	Tourism Almost Ideal Demand System	Tourism Demand Forecasting
Област	Tourism Hospitality	Tourism Hospitality
Семейство	Regression model	Regression model
Година на възникване	1980	2008
Създател	Angus Deaton & John Muellbauer; Gang Li, Haiyan Song & Stephen F. Witt (tourism application)	Haiyan Song; Gang Li; Stephen F. Witt
Тип	System-of-equations consumer demand model	Predictive time-series and econometric demand models
Основополагащ източник	Deaton, A., & Muellbauer, J. (1980). An Almost Ideal Demand System. American Economic Review, 70(3), 312-326.	Song, H., & Li, G. (2008). Tourism demand modelling and forecasting - A review of recent research. Tourism Management, 29(2), 203-220.
Други названия	Tourism AIDS Model, LAIDS Tourism Demand, Tourism Expenditure Allocation Model, System-of-Equations Tourism Demand	Tourist Arrivals Forecasting, SARIMA Tourism Forecasting, Tourism Demand Modelling and Forecasting, Econometric Tourism Forecasting
Свързани	4	4
Резюме	The Almost Ideal Demand System (AIDS), introduced by Angus Deaton and John Muellbauer in 1980, is a system of demand equations grounded in consumer theory that models how a budget is allocated across competing goods through their expenditure shares. Applied to tourism, AIDS treats a tourist's total travel budget as allocated across competing destinations (or expenditure categories), with each destination's budget share depending on relative prices and real total expenditure. Because it estimates all share equations jointly and can impose the restrictions implied by economic theory — adding-up, homogeneity, and symmetry — the model yields a consistent set of income (expenditure) and own- and cross-price elasticities, including how destinations substitute for one another. Gang Li, Haiyan Song, and Stephen Witt's dynamic linear AIDS application demonstrated its value for both explaining and forecasting tourism demand.	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.