Forecasting accuracy evaluation of tourist arrivals

被引：115

作者：

Hassani, Hossein ^{[1
]}

Silva, Emmanuel Sirimal ^{[2
]}

Antonakakis, Nikolaos ^{[3
,4
,5
]}

Filis, George ^{[6
]}

Gupta, Rangan ^{[7
]}

机构：

[1] Inst Int Energy Studies, No 65,Sayeh St,Vali Asr Ave, Tehran 193954757, Iran

[2] Univ Arts London, London Coll Fash, Fash Business Sch, 272 High Holborn, London WC1V 7EY, England

[3] Webster Vienna Private Univ, Praterstr 23, A-1020 Vienna, Austria

[4] Univ Portsmouth, Portsmouth Business Sch, Econ & Finance Subject Grp, Portland St, Portsmouth P01 3DE, Hants, England

[5] Johannes Kepler Univ Linz, Dept Econ, Altenbergerstrae 69, A-4040 Linz, Austria

[6] Bournemouth Univ, Accounting Finance & Econ Dept, 89 Holdenhurst Rd, Bournemouth BH8 8EB, Dorset, England

[7] Univ Pretoria, Fac Econ & Management Sci, Dept Econ, ZA-0002 Pretoria, South Africa

来源：

ANNALS OF TOURISM RESEARCH | 2017年 / 63卷

关键词：

Tourist arrivals; Forecasting; Singular spectrum analysis; Time series analysis; TIME-VARYING LINKAGES; ECONOMIC-GROWTH; NEURAL-NETWORK; DEMAND; SERIES; SEASONALITY; RECEIPTS; MODELS; FLOWS;

D O I：

10.1016/j.annals.2017.01.008

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper evaluates the use of several parametric and nonparametric forecasting techniques for predicting tourism &mild in selected European countries. We find that no single model can provide the best forecasts for any of the countries in the short-, medium- and long-run. The results, which are tested for statistical significance, enable forecasters to choose the most suitable model (from those evaluated here) based on the country and horizon for forecasting tourism demand. Should a single model be of interest, then, across all selected countries and horizons the Recurrent Singular Spectrum Analysis model is found to be the most efficient based on lowest overall forecasting error. Neural Networks and ARFIMA are found to be the worst performing models. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：112 / 127

页数：16

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