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

Nowman, K. Ben and Van Dellen, Stefan (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. ISSN 1354-8166

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Official URL: http://dx.doi.org/10.5367/te.2012.0144

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.

Item Type:Article
Research Community:University of Westminster > Westminster Business School
ID Code:9155
Deposited On:24 Feb 2011 14:34
Last Modified:28 May 2013 14:32

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