The Impact of Seasonal Unit Roots and Vector ARMA Modeling on Forecasting Monthly Tourism Flows
AbstractThe effect of imposing different numbers of unit roots on forecasting accuracy is examined using univariate ARMA models. To see whether additional information improves forecasting accuracy and increases the informative forecast horizon, we cross-relate the time series for inbound tourism in Sweden for different visitor categories and estimate vector ARMA models. The mean-squared forecast error for different filters indicates that models in which unit roots are imposed at all frequencies have the smallest forecast errors. The results from the vector ARMA models with all roots imposed indicate that the informative forecast horizon is greater than for the univariate models. Out-of-sample evaluations indicate, however, that the univariate modelling approach may be preferable.
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Bibliographic InfoPaper provided by Trade Union Institute for Economic Research in its series Working Paper Series with number 150.
Length: 22 pages
Date of creation: 01 Apr 1999
Date of revision: 01 Jul 2000
Publication status: Published in Tourism Economics, 2001, pages 117-133.
Seasonality; Tourism; Demand Analysis; VARMA; Forecasting;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
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