Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

Nowman, K.B. and Van Dellen, S. 2012. Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data. Tourism Economics. 18 (4), pp. 835-844.

TitleForecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data
AuthorsNowman, K.B. and Van Dellen, S.
Abstract

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

JournalTourism Economics
Journal citation18 (4), pp. 835-844
ISSN1354-8166
YearAug 2012
PublisherIP Publishing
Digital Object Identifier (DOI)doi:10.5367/te.2012.0144
Publication dates
PublishedAug 2012

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